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REVIEW 4 major objections 7 minor 2 cited by

Generative AI in Embodied Systems: System-Level Analysis of Performance, Efficiency and Scalability

T0 review · 4 major / 7 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Across 14 embodied AI agent systems, LLM-based planning and communication account for 70.2% of runtime latency on average.

desk verdict A genuinely useful first system-level latency map of embodied LLM agents, but the headline numbers are point estimates without variance, so treat them as directional. read the letter →

arxiv 2504.18945 v1 pith:6GPTW33Z submitted 2025-04-26 cs.RO

Zishen Wan , Jiayi Qian , Yuhang Du , Jason Jabbour , Yilun Du , Yang Katie Zhao , Arijit Raychowdhury , Tushar Krishna
show 1 more author
Vijay Janapa Reddi
This is my paper · ORCID
classification cs.RO
keywords embodiedAIlargelanguagemodelssystem-levelbenchmarkinglatencyanalysismulti-agentsystemsscalabilityworkloadcharacterizationmodulesensitivity
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish where time and reliability go in embodied AI agent systems—robots and simulated agents that perceive an environment, plan with a large language model, and execute physical or virtual actions. By benchmarking 14 published systems, it claims that LLM-based planning and communication dominate runtime, averaging 70.2% of per-step latency and producing end-to-end task times of 10 to 40 minutes. It also claims that memory and reflection modules are load-bearing for task success: removing memory raises required steps by 1.61x and lowers success by 27.7%, while removing reflection raises steps by 1.88x and lowers success by 33.3%. These results matter because they direct system architects to the modules that deserve optimization effort, rather than treating the entire pipeline as one undifferentiated cost.

What carries the argument

The central object is the six-module decomposition of an embodied agent—sensing, planning, communication, memory, reflection, and execution—paired with a 14-workload benchmark suite that instantiates these modules across four paradigms: single-agent modularized, end-to-end, centralized multi-agent, and decentralized multi-agent. The machinery does the essential work of turning "how well does embodied AI work" into per-module latency percentages and ablation-style sensitivity numbers: each system is run with a module disabled, and the change in steps, success rate, and runtime is attributed to that module. The named quantity that carries the argument is the average latency share of LLM-based modules, 70.2%, used as the headline target for optimization.

What would settle it

Run the same 14 workloads with, say, ten or more random seeds per configuration, recording step counts and success rates for the intact system and for each disabled-module variant. If the claimed gaps (e.g., 27.7% success drop without memory, 33.3% without reflection) fall inside the seed-to-seed spread or reverse sign on some workloads, the central sensitivity claims are not stable. A second check would swap the planning LLM for a different API or local model and re-measure the 70.2% latency share; if the share moves by tens of percentage points, the bottleneck claim is model-specific rather than a system-level law.

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Extended reading notes

Core claim

The paper's central empirical claim is a module-level attribution of latency and reliability across embodied agent workloads. Using a suite of 14 published embodied AI systems, it reports that LLM-based planning and communication—whether served through GPT-4 API calls or local Llama/LLaVA models—account for an average of 70.2% of runtime latency per step, with low-level execution also contributing large shares in systems such as RoCo (49.4%), DaDu-E (38.1%), and EmbodiedGPT (24.1%). It further claims that disabling the memory module increases required steps by 1.61x and cuts success rate by 27.7% across six tested systems, while disabling reflection increases steps by 1.88x and cuts success rate by 33.3%; disabling execution causes task failure. A secondary claim is that centralized multi-agent systems lose task success as agent count grows, while decentralized systems suffer communication overhead that scales quadratically, with end-to-end latency already at 10–40 minutes for a single task.

Load-bearing premise

The load-bearing premise is that the latency and success-rate numbers, each reported as a single value, are stable enough that differences of several percentage points or a factor of 1.6 in steps are real and not run-to-run noise, even though LLM outputs are stochastic and the paper reports no trial counts or confidence intervals.

Editorial extensions

If this is right

  • System optimization should target planning and communication first, using the paper's recommended levers of batching LLM queries, quantizing models, and deploying lightweight local models.
  • Memory and reflection are not optional extras; preserving their benefits while reducing their cost, for example through context summarization and dual short-term/long-term storage, follows directly from the ablation results.
  • Communication should become conditional: generate messages only when planning determines they are needed, because a large fraction of dialogue rounds are redundant.
  • Multi-agent scalability requires a hierarchical or clustered cooperation structure, since centralized planners lose task success with more agents and decentralized communication grows quadratically.
  • Low-level execution should be offloaded to specialized controllers rather than left to LLM-driven inference, because execution is both a major latency contributor and essential to task completion.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the 70.2% figure is roughly model-independent, future embodied-agent accelerators and schedulers should specialize in transformer inference for planning and dialogue rather than in perception or control; this is an extension the paper points toward but does not measure.
  • The memory ablation result suggests a concrete experiment: replacing raw observation and action histories with a compressed or learned memory should preserve most of the success-rate gain while reducing retrieval latency—something the paper recommends qualitatively but does not test.
  • The quadratic token growth in decentralized systems implies that API cost, not just wall-clock latency, will dominate at scale; reporting token consumption alongside runtime would let planners compare paradigms on an economic basis.
  • Because success rates are reported without trial counts or confidence intervals, a multi-seed re-run on a fixed task set would determine which sensitivity gaps are stable; the paper's numbers should be treated as hypotheses until then.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 7 minor

Summary. The paper presents a system-level workload characterization of embodied AI agents that use large language models. It proposes a taxonomy of four paradigms (single-agent modularized, single-agent end-to-end, multi-agent centralized, multi-agent decentralized), curates a suite of 14 embodied-agent systems spanning these paradigms, and benchmarks each for task success rate, runtime latency, per-module latency, and number of steps. The central empirical claim is that LLM-based planning and communication modules account for an average of 70.2% of total per-step latency across the 14 workloads (Section IV-A, Fig. 2a), and that memory and reflection modules are critical for task efficiency, with ablations showing 1.61× and 1.88× increases in steps and 27.7% and 33.3% drops in success rate, respectively (Section IV-B, Fig. 3). The paper also studies the effects of planning-model choice, memory capacity, prompt token growth, and agent count, and it concludes with a set of system optimization recommendations.

Significance. If the quantitative results are reliable, this would be a useful first system-level analysis for embodied-agent workloads, identifying LLM planning and communication as the dominant latency contributors and providing concrete targets for optimization. The curated workload suite, the taxonomy, and the qualitative observations (e.g., redundant dialogue, prompt-length growth, centralized versus decentralized scaling trade-offs) are potentially valuable to the architecture and systems community. However, the central numbers are presented as point estimates without any statistical grounding, and the aggregation methodology for the headline 70.2% figure is underspecified. These issues affect the credibility of the paper's main quantitative conclusions, so the current version cannot be accepted without substantial revision.

major comments (4)
  1. [Section III-E and Section IV-A] The experimental protocol is missing the number of trials, random seeds, or confidence intervals for any of the reported latency and success-rate numbers. The headline claim that LLM-based modules account for 70.2% of average latency (Fig. 2a) is a single point estimate, despite stochastic LLM API inference and high-variance task success. The same applies to the sensitivity results in Section IV-B (e.g., 1.61× steps and 27.7% success drop when memory is disabled). Without error bars or trial counts, these numbers cannot be distinguished from noise. I request that the authors specify how many independent runs per workload and per condition were performed, and that they report standard errors or confidence intervals on all point estimates, with significance tests for the ablation differences.
  2. [Section IV-A, Fig. 2a] The aggregation rule behind the 70.2% average latency fraction is not defined. It is unclear whether the per-module percentages are averaged per time step and then equally across workloads, or weighted by task latency, and how systems lacking a communication module (e.g., single-agent systems) are treated. Because the workload suite mixes single-agent and multi-agent systems, the reported average is sensitive to the suite composition and to the averaging choice. The authors should provide the exact formula, report per-workload module breakdowns (e.g., as a table or supplementary material), and discuss how the average would change if computed differently.
  3. [Section IV-B, Fig. 3] The module sensitivity analysis is performed on only six systems (CoELA, COMBO, COHERENT, RoCo, HMAS, JARVIS-1), not the full suite, and the text does not justify this selection or report per-system results. The aggregate statements such as 'disabling memory increases steps by an average of 1.61× and reduces success rate by 27.7%' are presented as averages with no error bars, and the claim that disabling communication 'does not have significant impact' appears to be a hypothesis (indeed, the text says 'We hypothesize') rather than a statistically supported finding. The authors should show per-system values, report variance, and test whether the observed differences are statistically meaningful.
  4. [Sections V and VI, Figs. 4-7] All results in the configuration and scalability analyses (local-model comparison, memory capacity, token-length evolution, and multi-agent scaling) are reported as point estimates with no variance. For example, Fig. 7 presents success-rate and latency curves versus number of agents without any error bars, although multi-agent task success is notoriously high-variance. I ask the authors to add replication counts and confidence intervals to these figures, or at a minimum to clearly label them as single-run illustrative results with a caveat that they are not statistically validated. The absence of any limitation statement about run-to-run variability is a gap in the paper's evidence.
minor comments (7)
  1. [Section I] The introduction says both 'the first comprehensive analysis' and 'one of the first works' when describing the contribution; these statements should be reconciled to avoid overclaiming.
  2. [Section III-E] The success rate metric is not formally defined; the authors should state how task success is determined for each workload and whether partial success is allowed.
  3. [Fig. 2a and Fig. 3] The figures would benefit from clearer axis labels and legends: Fig. 2a's horizontal axis is labeled in seconds, but the caption refers to percentage contributions; Fig. 3's marker legend for the modules is not described in the caption.
  4. [Table I] The action types 'V', 'T', and 'E' in Table I should be expanded in the caption (virtual, tool usage, physical) for readability.
  5. [References] References [4] and [82] appear to describe the same paper ('Aligning cyber space with physical world...'); please consolidate or distinguish them.
  6. [Section V-A] The text refers to 'Llama-3.1-8B' while Fig. 4 labels the model 'Llama-3-8B'; please align the notation.
  7. [Sections IV-VI] The recommendations (e.g., batching, quantization, planning-then-communication) are not validated by the experiments in this paper; I suggest labeling them clearly as informed hypotheses for future work rather than findings of this study.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the latency and sensitivity findings come from executing external workloads, and the optimization recommendations are not predictions fitted to those measurements.

full rationale

This paper is a workload characterization study. The central quantitative claims (the 70.2% average LLM-module latency share in Sec. IV-A; the 1.61x step increase and 27.7% success-rate drop when memory is disabled, and the 1.88x and 33.3% figures for reflection, in Sec. IV-B; and the scalability trends in Sec. VI) are read off from running fourteen published embodied-agent systems under the hardware setup described in Sec. III-E. No parameter is fitted to a subset of those measurements and then re-predicted as a derived quantity. The optimization recommendations (Recommendations 1-10) are qualitative design suggestions and are not validated by a predictive model built from the same benchmark data, so there is no self-fulfilling fit-to-prediction loop. Several references are by the present authors (e.g., refs. [2], [5], and [97]-[100]), but they appear in motivation and related-work contexts and are not invoked as a uniqueness theorem or as proof of the measured latency figures. The benchmarked suite includes CoELA [6] and COMBO [7], which share an author with this paper; that is a relevant conflict-of-interest caveat, but not circularity, because the measured data come from running those external systems rather than from the present paper's own claims. One substantive limitation is statistical, not circular: Sec. III-E reports no number of trials, seeds, or confidence intervals, so point estimates such as 70.2% lack demonstrated reproducibility; that is a correctness and robustness concern, not a reduction of an output to an input. No circular step was identified.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No fitted parameters are present; the numbers are measured averages. The four listed axioms are assumptions about representativeness, statistical stability, and ablation independence, all load-bearing for the empirical conclusions.

assumptions (3)
  • domain assumption The 14 selected systems are representative of embodied AI agents at large.
    Section III selects five single-agent and nine multi-agent systems across four paradigms, but no sampling criterion guarantees representativeness of the broader class of embodied LLM agents.
  • domain assumption Success rates and latency measurements are stable without repeated seeding or multiple trials.
    Section III-E defines metrics but does not report the number of episodes or confidence intervals, despite the stochastic nature of GPT-4 and Llama inference.
  • domain assumption Ablating a module isolates that module's contribution to performance.
    Section IV-B disables communication, memory, reflection, or execution and attributes observed performance differences to the removed module, assuming no interaction effects or re-planning cascades.

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Pith. "Pith review of Generative AI in Embodied Systems: System-Level Analysis of Performance, Efficiency and Scalability." pith.science (2026). https://pith.science/paper/6GPTW33Z

@misc{pith2026250418945,
  author       = {Pith},
  title        = {Pith review of: Generative AI in Embodied Systems: System-Level Analysis of Performance, Efficiency and Scalability},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6GPTW33Z}},
  note         = {Machine review of arXiv:2504.18945}
}
read the original abstract

Embodied systems, where generative autonomous agents engage with the physical world through integrated perception, cognition, action, and advanced reasoning powered by large language models (LLMs), hold immense potential for addressing complex, long-horizon, multi-objective tasks in real-world environments. However, deploying these systems remains challenging due to prolonged runtime latency, limited scalability, and heightened sensitivity, leading to significant system inefficiencies. In this paper, we aim to understand the workload characteristics of embodied agent systems and explore optimization solutions. We systematically categorize these systems into four paradigms and conduct benchmarking studies to evaluate their task performance and system efficiency across various modules, agent scales, and embodied tasks. Our benchmarking studies uncover critical challenges, such as prolonged planning and communication latency, redundant agent interactions, complex low-level control mechanisms, memory inconsistencies, exploding prompt lengths, sensitivity to self-correction and execution, sharp declines in success rates, and reduced collaboration efficiency as agent numbers increase. Leveraging these profiling insights, we suggest system optimization strategies to improve the performance, efficiency, and scalability of embodied agents across different paradigms. This paper presents the first system-level analysis of embodied AI agents, and explores opportunities for advancing future embodied system design.

Figures

Figures reproduced from arXiv: 2504.18945 by the authors.

Figure 1
Figure 1. Embodied AI Agents Paradigm. (a) The embodied AI agents typically consist of six key modules, where sensing module perceives the environment, planning module makes high-level plans, communication module generates messages, memory module stores the agent’s action, dialogue and world knowledge, execution module generates primitive actions, and reflection module reflects actions. The single-agent embodied AI systems ca… view at source ↗
Figure 2
Figure 2. Runtime Latency Analysis. (a) Average runtime percentage contributed by each module per time step, and (b) Total runtime latency per task, benchmarked across 14 embodied AI workloads. DMAS. DMAS [23] is a decentralized multi-agent embodied system for collaborative planning. Each agent uses GPT￾4 for planning and dialogue proceeds in rounds of turn￾taking among agents. Evaluated across BoxNet, Warehouse, and BoxLift … view at source ↗
Figure 4
Figure 4. Local Model Analysis. Task success rate and end-to-end task runtime under GPT-4 API call and Llama-3-8B local processing. memory through context summarization, and strengthening reflection with adaptive error correction. Offloading low-level execution to specialized controllers and adopting a hybrid planning framework can further boost task efficiency. V. SYSTEM CONFIGURATION IMPACT ANALYSIS Building on the characte… view at source ↗
Figures from the paper (2 more)
Figure 6
Figure 6. Figure 6: Prompt Token Length Analysis. Token length of LLM-based modules over time step across three embodied AI agent systems. ing irrelevant information, and compressing repeated patterns to keep the LLM context both efficient and relevant. D. Modular Embodied System: Pipelin…
Figure 7
Figure 7. Figure 7: Multi-Agent System Scalability Analysis. [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]

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Forward citations

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Reference graph

Works this paper leans on

105 extracted references · 42 canonical work pages · cited by 2 Pith papers

  1. [2]

    Thinking and moving: An efficient computing approach for inte- grated task and motion planning in cooperative embodied ai systems,

    Z. Wan, Y . Du, M. Ibrahim, Y . Zhao, T. Krishna, and A. Raychowdhury, “Thinking and moving: An efficient computing approach for inte- grated task and motion planning in cooperative embodied ai systems,” in Proceedings of the 43rd IEEE/ACM International Conference on Computer-Aided Design, pp. 1–7, 2024

    2024

  2. [5]

    Reca: Integrated acceleration for real-time and efficient cooperative embodied autonomous agents,

    Z. Wan, Y . Du, M. Ibrahim, J. Qian, J. Jabbour, Y . Zhao, T. Krishna, A. Raychowdhury, and V . J. Reddi, “Reca: Integrated acceleration for real-time and efficient cooperative embodied autonomous agents,” in Proceedings of the 30th ACM International Conference on Architec- tural Support for Programming Languages and Operating Systems, Volume 2, pp. 982–997, 2025

    2025

  3. [1]

    A survey of embodied ai: From simulators to research tasks,

    J. Duan, S. Yu, H. L. Tan, H. Zhu, and C. Tan, “A survey of embodied ai: From simulators to research tasks,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 2, pp. 230–244, 2022

    2022

  4. [3]

    A survey on robotics with foundation models: toward embodied ai,

    Z. Xu, K. Wu, J. Wen, J. Li, N. Liu, Z. Che, and J. Tang, “A survey on robotics with foundation models: toward embodied ai,” arXiv preprint arXiv:2402.02385, 2024

    arXiv 2024

  5. [4]

    Aligning cyber space with physical world: A com- prehensive survey on embodied ai,

    Y . Liu, W. Chen, Y . Bai, J. Luo, X. Song, K. Jiang, Z. Li, G. Zhao, J. Lin, G. Li, et al., “Aligning cyber space with physical world: A com- prehensive survey on embodied ai,” arXiv preprint arXiv:2407.06886 , 2024

    arXiv 2024

  6. [6]

    Building cooperative embodied agents modularly with large language models,

    H. Zhang, W. Du, J. Shan, Q. Zhou, Y . Du, J. B. Tenenbaum, T. Shu, and C. Gan, “Building cooperative embodied agents modularly with large language models,” International Conference on Learning Representations (ICLR), 2024

    2024

  7. [7]

    Combo: Compositional world models for embodied multi- agent cooperation,

    H. Zhang, Z. Wang, Q. Lyu, Z. Zhang, S. Chen, T. Shu, Y . Du, and C. Gan, “Combo: Compositional world models for embodied multi- agent cooperation,” arXiv preprint arXiv:2404.10775 , 2024

    arXiv 2024

  8. [8]

    Mindagent: Emergent gaming interaction,

    R. Gong, Q. Huang, X. Ma, H. V o, Z. Durante, Y . Noda, Z. Zheng, S.-C. Zhu, D. Terzopoulos, L. Fei-Fei, and G. Jianfeng, “Mindagent: Emergent gaming interaction,” International Conference on Learning Representations (ICLR), 2024

    2024

Show all 105 references
  1. [9]

    Embodied multi-modal agent trained by an llm from a parallel textworld,

    Y . Yang, T. Zhou, K. Li, D. Tao, L. Li, L. Shen, X. He, J. Jiang, and Y . Shi, “Embodied multi-modal agent trained by an llm from a parallel textworld,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 26275–26285, 2024

    2024

  2. [10]

    Agent-pro: Learning to evolve via policy-level reflection and optimization,

    W. Zhang, K. Tang, H. Wu, M. Wang, Y . Shen, G. Hou, Z. Tan, P. Li, Y . Zhuang, and W. Lu, “Agent-pro: Learning to evolve via policy-level reflection and optimization,” arXiv preprint arXiv:2402.17574 , 2024

    2024 arXiv

  3. [11]

    Film: Following instructions in language with modular methods,

    S. Y . Min, D. S. Chaplot, P. Ravikumar, Y . Bisk, and R. Salakhutdinov, “Film: Following instructions in language with modular methods,” arXiv preprint arXiv:2110.07342 , 2021

    2021 arXiv

  4. [12]

    Poni: Potential functions for objectgoal navigation with interaction-free learning,

    S. K. Ramakrishnan, D. S. Chaplot, Z. Al-Halah, J. Malik, and K. Grauman, “Poni: Potential functions for objectgoal navigation with interaction-free learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 18890– 18900, 2022

    2022

  5. [13]

    A persistent spa- tial semantic representation for high-level natural language instruction execution,

    V . Blukis, C. Paxton, D. Fox, A. Garg, and Y . Artzi, “A persistent spa- tial semantic representation for high-level natural language instruction execution,” in Conference on Robot Learning (CoRL) , pp. 706–717, PMLR, 2022

    2022

  6. [14]

    Octocache: Caching voxels for accelerating 3d occupancy mapping in autonomous systems,

    P. Chen, M. Li, Z. Wan, Y .-S. Hsiao, M. Yu, V . J. Reddi, and Z. Liu, “Octocache: Caching voxels for accelerating 3d occupancy mapping in autonomous systems,” in Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operat...

    2025

  7. [15]

    Language models as zero-shot planners: Extracting actionable knowledge for embodied agents,

    W. Huang, P. Abbeel, D. Pathak, and I. Mordatch, “Language models as zero-shot planners: Extracting actionable knowledge for embodied agents,” in International Conference on Machine Learning (ICML) , pp. 9118–9147, PMLR, 2022

    2022

  8. [16]

    Lever- aging pre-trained large language models to construct and utilize world models for model-based task planning,

    L. Guan, K. Valmeekam, S. Sreedharan, and S. Kambhampati, “Lever- aging pre-trained large language models to construct and utilize world models for model-based task planning,” Advances in Neural Informa- tion Processing Systems (NeurIPS) , vol. 36, pp. 79081–79094, 2023

    2023

  9. [17]

    Robogpt: an intelligent agent of making embodied long-term decisions for daily instruction tasks,

    Y . Chen, W. Cui, Y . Chen, M. Tan, X. Zhang, D. Zhao, and H. Wang, “Robogpt: an intelligent agent of making embodied long-term decisions for daily instruction tasks,” arXiv preprint arXiv:2311.15649 , 2023

    2023 arXiv

  10. [18]

    De- scribe, explain, plan and select: Interactive planning with large lan- guage models enables open-world multi-task agents,

    Z. Wang, S. Cai, G. Chen, A. Liu, X. Ma, and Y . Liang, “De- scribe, explain, plan and select: Interactive planning with large lan- guage models enables open-world multi-task agents,” arXiv preprint arXiv:2302.01560, 2023

    2023 arXiv

  11. [19]

    Large lan- guage models are zero-shot reasoners,

    T. Kojima, S. S. Gu, M. Reid, Y . Matsuo, and Y . Iwasawa, “Large lan- guage models are zero-shot reasoners,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 35, pp. 22199–22213, 2022

    2022

  12. [20]

    Tree of thoughts: Deliberate problem solving with large language models,

    S. Yao, D. Yu, J. Zhao, I. Shafran, T. Griffiths, Y . Cao, and K. Narasimhan, “Tree of thoughts: Deliberate problem solving with large language models,” Advances in Neural Information Processing Systems (NeurIPS), vol. 36, 2024

    2024

  13. [21]

    Graph of thoughts: Solving elaborate problems with large language models,

    M. Besta, N. Blach, A. Kubicek, R. Gerstenberger, M. Podstawski, L. Gianinazzi, J. Gajda, T. Lehmann, H. Niewiadomski, P. Nyczyk, et al. , “Graph of thoughts: Solving elaborate problems with large language models,” in Proceedings of the AAAI Conference on Artificial Intelligen...

    2024

  14. [22]

    Camel: Communicative agents for

    G. Li, H. Hammoud, H. Itani, D. Khizbullin, and B. Ghanem, “Camel: Communicative agents for” mind” exploration of large language model society,” Advances in Neural Information Processing Systems (NeurIPS), vol. 36, pp. 51991–52008, 2023

    2023

  15. [23]

    Scalable multi-robot collaboration with large language models: Centralized or decentralized systems?,

    Y . Chen, J. Arkin, Y . Zhang, N. Roy, and C. Fan, “Scalable multi-robot collaboration with large language models: Centralized or decentralized systems?,” in 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 4311–4317, IEEE, 2024

    2024

  16. [24]

    Embodied llm agents learn to cooperate in organized teams,

    X. Guo, K. Huang, J. Liu, W. Fan, N. V ´elez, Q. Wu, H. Wang, T. L. Griffiths, and M. Wang, “Embodied llm agents learn to cooperate in organized teams,” arXiv preprint arXiv:2403.12482 , 2024

    2024 arXiv

  17. [25]

    Language models meet world models: Embodied experiences enhance language models,

    J. Xiang, T. Tao, Y . Gu, T. Shu, Z. Wang, Z. Yang, and Z. Hu, “Language models meet world models: Embodied experiences enhance language models,” Advances in Neural Information Processing Systems (NeurIPS), vol. 36, 2024

    2024

  18. [26]

    Llm-planner: Few-shot grounded planning for embodied agents with large language models,

    C. H. Song, J. Wu, C. Washington, B. M. Sadler, W.-L. Chao, and Y . Su, “Llm-planner: Few-shot grounded planning for embodied agents with large language models,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , pp. 2998–3009, 2023

    2023

  19. [27]

    Jarvis-1: Open-world multi-task agents with memory-augmented multimodal language models,

    Z. Wang, S. Cai, A. Liu, Y . Jin, J. Hou, B. Zhang, H. Lin, Z. He, Z. Zheng, Y . Yang,et al., “Jarvis-1: Open-world multi-task agents with memory-augmented multimodal language models,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) , 2024

    2024

  20. [28]

    Towards general computer control: A multimodal agent for red dead redemption ii as a case study,

    W. Tan, Z. Ding, W. Zhang, B. Li, B. Zhou, J. Yue, H. Xia, J. Jiang, L. Zheng, X. Xu, et al. , “Towards general computer control: A multimodal agent for red dead redemption ii as a case study,” in ICLR 2024 Workshop on Large Language Model (LLM) Agents , 2024

    2024

  21. [29]

    Do as i can, not as i say: Grounding language in robotic affordances,

    M. Ahn, A. Brohan, N. Brown, Y . Chebotar, O. Cortes, B. David, C. Finn, C. Fu, K. Gopalakrishnan, K. Hausman, et al., “Do as i can, not as i say: Grounding language in robotic affordances,”arXiv preprint arXiv:2204.01691, 2022

    2022 arXiv

  22. [30]

    Roco: Dialectic multi-robot col- laboration with large language models,

    Z. Mandi, S. Jain, and S. Song, “Roco: Dialectic multi-robot col- laboration with large language models,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) , pp. 286–299, IEEE, 2024

    2024

  23. [31]

    Coherent: Collaboration of heterogeneous multi-robot system with large language models,

    K. Liu, Z. Tang, D. Wang, Z. Wang, B. Zhao, and X. Li, “Coherent: Collaboration of heterogeneous multi-robot system with large language models,” arXiv preprint arXiv:2409.15146 , 2024

    2024 arXiv

  24. [32]

    Self-contrast: Better reflection through inconsistent solving perspectives,

    W. Zhang, Y . Shen, L. Wu, Q. Peng, J. Wang, Y . Zhuang, and W. Lu, “Self-contrast: Better reflection through inconsistent solving perspectives,” arXiv preprint arXiv:2401.02009 , 2024

    2024 arXiv

  25. [33]

    Plan, eliminate, and track–language models are good teachers for embodied agents,

    Y . Wu, S. Y . Min, Y . Bisk, R. Salakhutdinov, A. Azaria, Y . Li, T. Mitchell, and S. Prabhumoye, “Plan, eliminate, and track–language models are good teachers for embodied agents,” arXiv preprint arXiv:2305.02412, 2023

    2023 arXiv

  26. [34]

    Large language models as common- sense knowledge for large-scale task planning,

    Z. Zhao, W. S. Lee, and D. Hsu, “Large language models as common- sense knowledge for large-scale task planning,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 36, 2024

    2024

  27. [35]

    Integrated task and motion planning,

    C. R. Garrett, R. Chitnis, R. Holladay, B. Kim, T. Silver, L. P. Kaelbling, and T. Lozano-P´erez, “Integrated task and motion planning,” Annual review of control, robotics, and autonomous systems , vol. 4, no. 1, pp. 265–293, 2021

    2021

  28. [36]

    Mobile-agent: Autonomous multi-modal mobile device agent with visual perception,

    J. Wang, H. Xu, J. Ye, M. Yan, W. Shen, J. Zhang, F. Huang, and J. Sang, “Mobile-agent: Autonomous multi-modal mobile device agent with visual perception,” arXiv preprint arXiv:2401.16158 , 2024

    2024 arXiv

  29. [37]

    Appagent: Multimodal agents as smartphone users,

    C. Zhang, Z. Yang, J. Liu, Y . Han, X. Chen, Z. Huang, B. Fu, and G. Yu, “Appagent: Multimodal agents as smartphone users,” arXiv preprint arXiv:2312.13771, 2023

    2023 arXiv

  30. [38]

    V oyager: An open-ended embodied agent with large language models,

    G. Wang, Y . Xie, Y . Jiang, A. Mandlekar, C. Xiao, Y . Zhu, L. Fan, and A. Anandkumar, “V oyager: An open-ended embodied agent with large language models,” arXiv preprint arXiv:2305.16291 , 2023

    2023 arXiv

  31. [39]

    Mp5: A multi-modal open-ended embodied system in minecraft via active perception,

    Y . Qin, E. Zhou, Q. Liu, Z. Yin, L. Sheng, R. Zhang, Y . Qiao, and J. Shao, “Mp5: A multi-modal open-ended embodied system in minecraft via active perception,” in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 16307–16316, IEEE, 2024

    2024

  32. [40]

    Rila: Reflective and imaginative language agent for zero-shot semantic audio-visual navigation,

    Z. Yang, J. Liu, P. Chen, A. Cherian, T. K. Marks, J. Le Roux, and C. Gan, “Rila: Reflective and imaginative language agent for zero-shot semantic audio-visual navigation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 16251–...

    2024

  33. [41]

    See and think: Embodied agent in virtual environment,

    Z. Zhao, W. Chai, X. Wang, B. Li, S. Hao, S. Cao, T. Ye, and G. Wang, “See and think: Embodied agent in virtual environment,” in European Conference on Computer Vision (ECCV), pp. 187–204, Springer, 2025

    2025

  34. [42]

    Embodiedgpt: Vision-language pre-training via embodied chain of thought,

    Y . Mu, Q. Zhang, M. Hu, W. Wang, M. Ding, J. Jin, B. Wang, J. Dai, Y . Qiao, and P. Luo, “Embodiedgpt: Vision-language pre-training via embodied chain of thought,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 36, 2024

    2024

  35. [43]

    Dadu-e: Rethinking the role of large language model in robotic computing pipeline,

    W. Sun, S. Hou, Z. Wang, B. Yu, S. Liu, X. Yang, S. Liang, Y . Gan, and Y . Han, “Dadu-e: Rethinking the role of large language model in robotic computing pipeline,” arXiv preprint arXiv:2412.01663 , 2024

    2024 arXiv

  36. [44]

    Minedojo: Building open- ended embodied agents with internet-scale knowledge,

    L. Fan, G. Wang, Y . Jiang, A. Mandlekar, Y . Yang, H. Zhu, A. Tang, D.-A. Huang, Y . Zhu, and A. Anandkumar, “Minedojo: Building open- ended embodied agents with internet-scale knowledge,” Advances in Neural Information Processing Systems (NeurIPS), vol. 35, pp. 18343– 18362, 2022

    2022

  37. [45]

    Luban: Building open-ended cre- ative agents via autonomous embodied verification,

    Y . Guo, S. Peng, J. Guo, D. Huang, X. Zhang, R. Zhang, Y . Hao, L. Li, Z. Tian, M. Gao, et al. , “Luban: Building open-ended cre- ative agents via autonomous embodied verification,” arXiv preprint arXiv:2405.15414, 2024

    2024 arXiv

  38. [46]

    Metagpt: Meta programming for multi-agent collaborative framework,

    S. Hong, X. Zheng, J. Chen, Y . Cheng, J. Wang, C. Zhang, Z. Wang, S. K. S. Yau, Z. Lin, L. Zhou, et al. , “Metagpt: Meta programming for multi-agent collaborative framework,” arXiv preprint arXiv:2308.00352, 2023

    2023 arXiv

  39. [47]

    Mobile-agent-v2: Mobile device operation assistant with effective navigation via multi-agent collaboration,

    J. Wang, H. Xu, H. Jia, X. Zhang, M. Yan, W. Shen, J. Zhang, F. Huang, and J. Sang, “Mobile-agent-v2: Mobile device operation assistant with effective navigation via multi-agent collaboration,” arXiv preprint arXiv:2406.01014, 2024

    2024 arXiv

  40. [48]

    Rt-2: Vision- language-action models transfer web knowledge to robotic control,

    A. Brohan, N. Brown, J. Carbajal, Y . Chebotar, X. Chen, K. Choro- manski, T. Ding, D. Driess, A. Dubey, C. Finn, et al. , “Rt-2: Vision- language-action models transfer web knowledge to robotic control,” in Proceedings of Machine Learning Research (PMLR) , vol. 229, pp. 2165–...

    2023

  41. [49]

    Towards generalist robot policies: What matters in building vision-language-action models,

    X. Li, P. Li, M. Liu, D. Wang, J. Liu, B. Kang, X. Ma, T. Kong, H. Zhang, and H. Liu, “Towards generalist robot policies: What matters in building vision-language-action models,” 2024

    2024

  42. [50]

    Gaia-1: A generative world model for autonomous driving,

    A. Hu, L. Russell, H. Yeo, Z. Murez, G. Fedoseev, A. Kendall, J. Shotton, and G. Corrado, “Gaia-1: A generative world model for autonomous driving,” arXiv preprint arXiv:2309.17080 , 2023

    2023 arXiv

  43. [51]

    3d-vla: A 3d vision-language-action generative world model,

    H. Zhen, X. Qiu, P. Chen, J. Yang, X. Yan, Y . Du, Y . Hong, and C. Gan, “3d-vla: A 3d vision-language-action generative world model,” arXiv preprint arXiv:2403.09631, 2024

    2024 arXiv

  44. [52]

    Octo: An open-source generalist robot policy,

    O. M. Team, D. Ghosh, H. Walke, K. Pertsch, K. Black, O. Mees, S. Dasari, J. Hejna, T. Kreiman, C. Xu, et al., “Octo: An open-source generalist robot policy,” arXiv preprint arXiv:2405.12213 , 2024

    2024 arXiv

  45. [53]

    Diffusion policy: Visuomotor policy learning via action diffusion,

    C. Chi, Z. Xu, S. Feng, E. Cousineau, Y . Du, B. Burchfiel, R. Tedrake, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” The International Journal of Robotics Research (IJRR) , p. 02783649241273668, 2023

    2023

  46. [54]

    Controlling large language model-based agents for large-scale decision-making: An actor-critic approach,

    B. Zhang, H. Mao, J. Ruan, Y . Wen, Y . Li, S. Zhang, Z. Xu, D. Li, Z. Li, R. Zhao, et al. , “Controlling large language model-based agents for large-scale decision-making: An actor-critic approach,” arXiv preprint arXiv:2311.13884, 2023

    2023 arXiv

  47. [55]

    Algpt: Multi-agent cooperative framework for open-vocabulary multi-modal auto-annotating in autonomous driving,

    Y . Zhou, X. Cheng, Q. Zhang, L. Wang, W. Ding, X. Xue, C. Luo, and J. Pu, “Algpt: Multi-agent cooperative framework for open-vocabulary multi-modal auto-annotating in autonomous driving,” IEEE Transac- tions on Intelligent Vehicles , 2024

    2024

  48. [56]

    Towards efficient llm grounding for embodied multi-agent collaboration,

    Y . Zhang, S. Yang, C. Bai, F. Wu, X. Li, X. Li, and Z. Wang, “Towards efficient llm grounding for embodied multi-agent collaboration,” arXiv preprint arXiv:2405.14314, 2024

    2024

  49. [57]

    Co-navgpt: Multi-robot cooperative vi- sual semantic navigation using large language models,

    B. Yu, H. Kasaei, and M. Cao, “Co-navgpt: Multi-robot cooperative vi- sual semantic navigation using large language models,” arXiv preprint arXiv:2310.07937, 2023

    2023 arXiv

  50. [58]

    Affordable generative agents,

    Y . Yu, Q. Zhang, J. Li, Q. Fu, and D. Ye, “Affordable generative agents,” arXiv preprint arXiv:2402.02053 , 2024

    2024 arXiv

  51. [59]

    Building the future of responsible ai: A pattern-oriented reference architecture for designing large language model based agents,

    Q. Lu, L. Zhu, X. Xu, Z. Xing, S. Harrer, and J. Whittle, “Building the future of responsible ai: A pattern-oriented reference architecture for designing large language model based agents,” arXiv preprint arXiv:2311.13148, 2023

    2023 arXiv

  52. [60]

    Agentverse: Facilitating multi- agent collaboration and exploring emergent behaviors,

    W. Chen, Y . Su, J. Zuo, C. Yang, C. Yuan, C.-M. Chan, H. Yu, Y . Lu, Y .-H. Hung, C. Qian, et al. , “Agentverse: Facilitating multi- agent collaboration and exploring emergent behaviors,” in The Twelfth International Conference on Learning Representations (ICLR) , 2023

    2023

  53. [61]

    Koma: Knowledge-driven multi-agent framework for autonomous driving with large language models,

    K. Jiang, X. Cai, Z. Cui, A. Li, Y . Ren, H. Yu, H. Yang, D. Fu, L. Wen, and P. Cai, “Koma: Knowledge-driven multi-agent framework for autonomous driving with large language models,” IEEE Transactions on Intelligent Vehicles, 2024

    2024

  54. [62]

    Relay policy learning: Solving long-horizon tasks via imitation and reinforce- ment learning,

    A. Gupta, V . Kumar, C. Lynch, S. Levine, and K. Hausman, “Relay policy learning: Solving long-horizon tasks via imitation and reinforce- ment learning,” arXiv preprint arXiv:1910.11956 , 2019

    1910 arXiv

  55. [63]

    Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning,

    T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine, “Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning,” in Conference on Robot Learning (CoRL) , pp. 1094–1100, PMLR, 2020

    2020

  56. [64]

    Virtualhome: Simulating household activities via programs,

    X. Puig, K. Ra, M. Boben, J. Li, T. Wang, S. Fidler, and A. Tor- ralba, “Virtualhome: Simulating household activities via programs,” in Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 8494–8502, 2018

    2018

  57. [65]

    Mcu: A task-centric frame- work for open-ended agent evaluation in minecraft,

    H. Lin, Z. Wang, J. Ma, and Y . Liang, “Mcu: A task-centric frame- work for open-ended agent evaluation in minecraft,” arXiv preprint arXiv:2310.08367, 2023

    2023 arXiv

  58. [66]

    Video pretraining (vpt): Learning to act by watching unlabeled online videos,

    B. Baker, I. Akkaya, P. Zhokov, J. Huizinga, J. Tang, A. Ecoffet, B. Houghton, R. Sampedro, and J. Clune, “Video pretraining (vpt): Learning to act by watching unlabeled online videos,” Advances in Neural Information Processing Systems (NeurIPS), vol. 35, pp. 24639– 24654, 2022

    2022

  59. [67]

    Alfworld: Aligning text and embodied environments for interactive learning,

    M. Shridhar, X. Yuan, M.-A. C ˆot´e, Y . Bisk, A. Trischler, and M. Hausknecht, “Alfworld: Aligning text and embodied environments for interactive learning,” arXiv preprint arXiv:2010.03768 , 2020

    2010 arXiv

  60. [68]

    Watch-and-help: A challenge for social perception and human-ai collaboration,

    X. Puig, T. Shu, S. Li, Z. Wang, Y .-H. Liao, J. B. Tenenbaum, S. Fidler, and A. Torralba, “Watch-and-help: A challenge for social perception and human-ai collaboration,” in International Conference on Learning Representations (ICLR), 2021

    2021

  61. [69]

    Behavior-1k: A benchmark for embodied ai with 1,000 everyday activities and realistic simulation,

    C. Li, R. Zhang, J. Wong, C. Gokmen, S. Srivastava, R. Mart ´ın-Mart´ın, C. Wang, G. Levine, M. Lingelbach, J. Sun, et al. , “Behavior-1k: A benchmark for embodied ai with 1,000 everyday activities and realistic simulation,” in Conference on Robot Learning (CoRL) , pp. 80–93, ...

    2023

  62. [70]

    The threedworld transport challenge: A visually guided task-and-motion planning benchmark towards physically realistic embodied ai,

    C. Gan, S. Zhou, J. Schwartz, S. Alter, A. Bhandwaldar, D. Gutfreund, D. L. Yamins, J. J. DiCarlo, J. McDermott, A. Torralba, et al. , “The threedworld transport challenge: A visually guided task-and-motion planning benchmark towards physically realistic embodied ai,” in 2022 ...

    2022

  63. [71]

    Three- dworld: A platform for interactive multi-modal physical simulation,

    C. Gan, J. Schwartz, S. Alter, D. Mrowca, M. Schrimpf, J. Traer, J. De Freitas, J. Kubilius, A. Bhandwaldar, N. Haber, et al. , “Three- dworld: A platform for interactive multi-modal physical simulation,” in Thirty-fifth Conference on Neural Information Processing Systems (Neu...

    2021

  64. [72]

    Anygrasp: Robust and efficient grasp perception in spatial and temporal domains,

    H.-S. Fang, C. Wang, H. Fang, M. Gou, J. Liu, H. Yan, W. Liu, Y . Xie, and C. Lu, “Anygrasp: Robust and efficient grasp perception in spatial and temporal domains,” IEEE Transactions on Robotics (T-RO), 2023

    2023

  65. [73]

    Grounding dino: Marrying dino with grounded pre-training for open-set object detection,

    S. Liu, Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, Q. Jiang, C. Li, J. Yang, H. Su, et al., “Grounding dino: Marrying dino with grounded pre-training for open-set object detection,” in European Conference on Computer Vision (ECCV) , pp. 38–55, Springer, 2025

    2025

  66. [74]

    Open-vocabulary object de- tection via vision and language knowledge distillation,

    X. Gu, T.-Y . Lin, W. Kuo, and Y . Cui, “Open-vocabulary object de- tection via vision and language knowledge distillation,” arXiv preprint arXiv:2104.13921, 2021

    2021 arXiv

  67. [75]

    Learning to act from actionless videos through dense correspondences,

    P.-C. Ko, J. Mao, Y . Du, S.-H. Sun, and J. B. Tenenbaum, “Learning to act from actionless videos through dense correspondences,” arXiv preprint arXiv:2310.08576, 2023

    2023 arXiv

  68. [76]

    Visual instruction tuning,

    H. Liu, C. Li, Q. Wu, and Y . J. Lee, “Visual instruction tuning,” Advances in neural information processing systems (NeurIPS) , vol. 36, 2024

    2024

  69. [77]

    Simple open-vocabulary object detection,

    M. Minderer, A. Gritsenko, A. Stone, M. Neumann, D. Weissenborn, A. Dosovitskiy, A. Mahendran, A. Arnab, M. Dehghani, Z. Shen, et al., “Simple open-vocabulary object detection,” in European Conference on Computer Vision (ECCV) , pp. 728–755, Springer, 2022

    2022

  70. [78]

    Optimal path planning using rrt* based approaches: a survey and future directions,

    I. Noreen, A. Khan, and Z. Habib, “Optimal path planning using rrt* based approaches: a survey and future directions,” International Journal of Advanced Computer Science and Applications , vol. 7, no. 11, 2016

    2016

  71. [79]

    Path planning for autonomous mobile robots: A review,

    J. R. Sanchez-Ibanez, C. J. P ´erez-del Pulgar, and A. Garc ´ıa-Cerezo, “Path planning for autonomous mobile robots: A review,” Sensors, vol. 21, no. 23, p. 7898, 2021

    2021

  72. [80]

    Awq: Activation-aware weight quan- tization for on-device llm compression and acceleration,

    J. Lin, J. Tang, H. Tang, S. Yang, W.-M. Chen, W.-C. Wang, G. Xiao, X. Dang, C. Gan, and S. Han, “Awq: Activation-aware weight quan- tization for on-device llm compression and acceleration,” Proceedings of Machine Learning and Systems (MLSys) , vol. 6, pp. 87–100, 2024

    2024

  73. [81]

    MLC-LLM

    M. team, “MLC-LLM.” https://github.com/mlc-ai/mlc-llm, 2023

    2023

  74. [82]

    Aligning cyber space with physical world: A comprehensive survey on embodied ai,

    Y . Liu, W. Chen, Y . Bai, X. Liang, G. Li, W. Gao, and L. Lin, “Aligning cyber space with physical world: A comprehensive survey on embodied ai,” 2024

    2024

  75. [83]

    Neurobench: Advancing neuromorphic computing through collaborative, fair and representative benchmarking,

    J. Yik, S. H. Ahmed, Z. Ahmed, B. Anderson, A. G. Andreou, C. Bartolozzi, A. Basu, D. d. Blanken, P. Bogdan, S. Bohte, et al. , “Neurobench: Advancing neuromorphic computing through collaborative, fair and representative benchmarking,” arXiv preprint arXiv:2304.04640, 2023

    2023 arXiv

  76. [84]

    A 73.53 tops/w 14.74 tops heterogeneous rram in-memory and sram near-memory soc for hybrid frame and event-based target tracking,

    M. Chang, A. S. Lele, S. D. Spetalnick, B. Crafton, S. Konno, Z. Wan, A. Bhat, W.-S. Khwa, Y .-D. Chih, M.-F. Chang,et al., “A 73.53 tops/w 14.74 tops heterogeneous rram in-memory and sram near-memory soc for hybrid frame and event-based target tracking,” in 2023 IEEE Internat...

    2023

  77. [85]

    ubrain: A unary brain computer interface,

    D. Wu, J. Li, Z. Pan, Y . Kim, and J. S. Miguel, “ubrain: A unary brain computer interface,” in Proceedings of the 49th Annual International Symposium on Computer Architecture (ISCA) , pp. 468–481, 2022

    2022

  78. [86]

    Xrbench: An extended reality (xr) machine learning benchmark suite for the metaverse,

    H. Kwon, K. Nair, J. Seo, J. Yik, D. Mohapatra, D. Zhan, J. Song, P. Capak, P. Zhang, P. Vajda,et al., “Xrbench: An extended reality (xr) machine learning benchmark suite for the metaverse,” Proceedings of Machine Learning and Systems (MLSys) , vol. 5, 2023

    2023

  79. [87]

    E-gaze: Gaze estimation with event camera,

    N. Li, M. Chang, and A. Raychowdhury, “E-gaze: Gaze estimation with event camera,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024

    2024

  80. [88]

    Roofline model for uavs: A bottleneck analysis tool for onboard compute characterization of autonomous unmanned aerial vehicles,

    S. Krishnan, Z. Wan, K. Bhardwaj, N. Jadhav, A. Faust, and V . J. Reddi, “Roofline model for uavs: A bottleneck analysis tool for onboard compute characterization of autonomous unmanned aerial vehicles,” in 2022 IEEE International Symposium on Performance Analysis of Systems a...

    2022

  81. [89]

    An energy-efficient and runtime-reconfigurable fpga-based accelerator for robotic localization systems,

    Q. Liu, Z. Wan, B. Yu, W. Liu, S. Liu, and A. Raychowdhury, “An energy-efficient and runtime-reconfigurable fpga-based accelerator for robotic localization systems,” in 2022 IEEE Custom Integrated Circuits Conference (CICC), pp. 01–02, IEEE, 2022

    2022

  82. [90]

    Automatic domain- specific soc design for autonomous unmanned aerial vehicles,

    S. Krishnan, Z. Wan, K. Bhardwaj, P. Whatmough, A. Faust, S. Neu- man, G.-Y . Wei, D. Brooks, and V . J. Reddi, “Automatic domain- specific soc design for autonomous unmanned aerial vehicles,” in 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO), pp. 300–...

    2022

  83. [91]

    Mulberry: Enabling bit-error robustness for energy-efficient multi-agent autonomous systems,

    Z. Wan, N. Chandramoorthy, K. Swaminathan, P.-Y . Chen, K. Bhard- waj, V . J. Reddi, and A. Raychowdhury, “Mulberry: Enabling bit-error robustness for energy-efficient multi-agent autonomous systems,” in Proceedings of the 29th ACM International Conference on Architec- tural S...

    2024

  84. [92]

    Robomorphic computing: a design methodology for domain-specific accelerators parameterized by robot morphology,

    S. M. Neuman, B. Plancher, T. Bourgeat, T. Tambe, S. Devadas, and V . J. Reddi, “Robomorphic computing: a design methodology for domain-specific accelerators parameterized by robot morphology,” in Proceedings of the 26th ACM International Conference on Archi- tectural Support ...

    2021

  85. [93]

    An energy-efficient quad-camera visual system for autonomous machines on fpga platform,

    Z. Wan, Y . Zhang, A. Raychowdhury, B. Yu, Y . Zhang, and S. Liu, “An energy-efficient quad-camera visual system for autonomous machines on fpga platform,” in 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS) , pp. 1–4, IEEE, 2021

    2021

  86. [94]

    Roboshape: Using topology patterns to scalably and flexibly deploy accelerators across robots,

    S. M. Neuman, R. Ghosal, T. Bourgeat, B. Plancher, and V . J. Reddi, “Roboshape: Using topology patterns to scalably and flexibly deploy accelerators across robots,” in Proceedings of the 50th Annual International Symposium on Computer Architecture (ISCA) , pp. 1–13, 2023

    2023

  87. [95]

    Robotperf: An open-source, vendor-agnostic, benchmarking suite for evaluating robotics computing system per- formance,

    V . Mayoral-Vilches, J. Jabbour, Y .-S. Hsiao, Z. Wan, M. Crespo- ´Alvarez, M. Stewart, J. M. Reina-Mu ˜noz, P. Nagras, G. Vikhe, M. Bakhshalipour, et al., “Robotperf: An open-source, vendor-agnostic, benchmarking suite for evaluating robotics computing system per- formance,” ...

    2024

  88. [96]

    Orianna: An accelerator generation framework for optimization-based robotic applications,

    Y . Hao, Y . Gan, B. Yu, Q. Liu, Y . Han, Z. Wan, and S. Liu, “Orianna: An accelerator generation framework for optimization-based robotic applications,” in Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Sy...

    2024

  89. [97]

    Towards cognitive ai systems: Workload and characterization of neuro-symbolic ai,

    Z. Wan, C.-K. Liu, H. Yang, R. Raj, C. Li, H. You, Y . Fu, C. Wan, A. Samajdar, Y . C. Lin,et al., “Towards cognitive ai systems: Workload and characterization of neuro-symbolic ai,” in 2024 IEEE Interna- tional Symposium on Performance Analysis of Systems and Software (ISPASS...

    2024

  90. [98]

    Towards efficient neuro-symbolic ai: From workload characterization to hardware architecture,

    Z. Wan, C.-K. Liu, H. Yang, R. Raj, C. Li, H. You, Y . Fu, C. Wan, S. Li, Y . Kim, et al. , “Towards efficient neuro-symbolic ai: From workload characterization to hardware architecture,”IEEE Transactions on Circuits and Systems for Artificial Intelligence , 2024

    2024

  91. [99]

    Special session: Neuro-symbolic architecture meets large language models: A memory-centric perspective,

    M. Ibrahim, Z. Wan, H. Li, P. Panda, T. Krishna, P. Kanerva, Y . Chen, and A. Raychowdhury, “Special session: Neuro-symbolic architecture meets large language models: A memory-centric perspective,” in 2024 International Conference on Hardware/Software Codesign and System Synth...

    2024

  92. [100]

    Cogsys: Efficient and scalable neurosymbolic cognition system via algorithm-hardware co-design,

    Z. Wan, H. Yang, R. Raj, C.-K. Liu, A. Samajdar, A. Raychowd- hury, and T. Krishna, “Cogsys: Efficient and scalable neurosymbolic cognition system via algorithm-hardware co-design,” in 2025 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 775–...

    2025

  93. [101]

    Genax: A genome sequencing accelerator,

    D. Fujiki, A. Subramaniyan, T. Zhang, Y . Zeng, R. Das, D. Blaauw, and S. Narayanasamy, “Genax: A genome sequencing accelerator,” in 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA), pp. 69–82, IEEE, 2018

    2018

  94. [102]

    Seedex: A genome sequencing accelerator for optimal alignments in subminimal space,

    D. Fujiki, S. Wu, N. Ozog, K. Goliya, D. Blaauw, S. Narayanasamy, and R. Das, “Seedex: A genome sequencing accelerator for optimal alignments in subminimal space,” in 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), pp. 937–950, IEEE, 2020

    2020

  95. [103]

    Mega evolving graph accelerator,

    C. Gao, M. Afarin, S. Rahman, N. Abu-Ghazaleh, and R. Gupta, “Mega evolving graph accelerator,” in Proceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO) , pp. 310–323, 2023

    2023

  96. [104]

    Systolic tensor ar- ray: An efficient structured-sparse gemm accelerator for mobile cnn inference,

    Z.-G. Liu, P. N. Whatmough, and M. Mattina, “Systolic tensor ar- ray: An efficient structured-sparse gemm accelerator for mobile cnn inference,” IEEE Computer Architecture Letters (CAL) , vol. 19, no. 1, pp. 34–37, 2020

    2020

  97. [105]

    S2ta: Exploiting structured sparsity for energy-efficient mobile cnn acceleration,

    Z.-G. Liu, P. N. Whatmough, Y . Zhu, and M. Mattina, “S2ta: Exploiting structured sparsity for energy-efficient mobile cnn acceleration,” in 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA), pp. 573–586, IEEE, 2022

    2022

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.