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REVIEW 6 major objections 4 minor 2 cited by

ConceptBot claims that injecting structured commonsense relations from a knowledge graph into LLM prompts lets a robot generate feasible, risk-aware pick-and-place policies from ambiguous natural-language instructions, without any fine-tuni

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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Using ConceptNet-augmented prompts, ConceptBot reports 87% vs 31% success on implicit tasks and 76% vs 15% on risk-aware tasks over a re-implemented SayCan baseline, with an 80% SafeAgentBench score.

T0 review reviewed 2026-08-05 challenge →

load-bearing objection A useful KG-grounded planner with a strong ablation, but the headline SayCan comparisons rest on a re-implementation that is not a faithful proxy, so the reported margins should be read with caution. the 6 major comments →

arxiv 2509.00570 v1 pith:H66ZHC7L submitted 2025-08-30 cs.RO

ConceptBot: Enhancing Robot's Autonomy through Task Decomposition with Large Language Models and Knowledge Graph

Alessandro Leanza , Angelo Moroncelli , Giuseppe Vizzari , Francesco Braghin , Loris Roveda , Blerina Spahiu This is my paper
classification cs.RO
keywords robotic task planningtask decompositionlarge language modelsknowledge graphsConceptNetpick-and-placerisk-aware planningcommonsense reasoning
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

The paper proposes ConceptBot, a modular robotic planner that combines a large language model with the ConceptNet knowledge graph at inference time. Its central claim is that enriching both detected objects and user instructions with filtered semantic relations—fragile, dangerous, toxic, made of—makes LLM-generated plans more feasible and risk-aware, especially when the instruction is implicit or ambiguous. The system requires no additional training. In the paper's evaluations, ConceptBot reports 100% success on explicit tasks, 87% on implicit tasks, and 76% on risk-aware tasks, against a re-evaluated SayCan baseline at 31% and 15%, plus an 80% score on SafeAgentBench. If correct, this points to a lightweight way to reduce hallucinations and unsafe actions in LLM-based robot planning.

Core claim

The central claim is that LLM-based robotic task planning fails not for lack of language ability but for lack of grounded commonsense knowledge about objects and their affordances. ConceptBot addresses this by (i) querying ConceptNet for each detected object, filtering triples by cosine similarity with target properties, (ii) extracting and disambiguating keywords from the user request, and (iii) combining LLM action scores with an affordance score that penalizes fragile, dangerous, or unstable choices. The paper argues this lets the planner choose safer alternatives—safety scissors over scissors for a child, or placing food beside rather than inside a fragile ceramic bowl—and infer unstated

What carries the argument

The load-bearing mechanism is the combination of three modules: Object Property Extraction (OPE), User Request Processing (URP), and the Planner. OPE retrieves ConceptNet triples for detected objects, converts them to embeddings, and filters them with a 0.75 cosine threshold before injecting them into the LLM system prompt. URP extracts keywords from the user request and enriches the context with related concepts. The Planner then multiplies an LLM vote score by an affordance score that combines detection confidence, gripper fit, and property penalties, selecting the action with the highest product.

Load-bearing premise

The head-to-head comparison assumes that the re-implemented SayCan baseline, run with a newer LLM and adapted low-level actions, is a faithful proxy for the published system, and that the 'most secure and stable' success criterion is a fair measure of correctness.

What would settle it

Run both systems on the same held-out set of implicit and risk-aware tasks with independent human raters who do not know which policy came from which system, using the original SayCan code and action set instead of a re-implementation; if ConceptBot's margin over the baseline shrinks to noise, the central claim fails. Additionally, remove the ConceptNet relation injection while keeping the OPE and URP prompts otherwise identical; the paper's own ablation reports drops of 18–30%, so an independent replication of that ablation would test whether the injected relations are the actual cause.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Robots could handle ambiguous or implicit requests without retraining, by enriching prompts with structured commonsense facts at inference time.
  • Risk-aware behavior becomes a prompt-level property: the same planner can avoid microwaving aluminum trays, giving knives to children, or placing fragile objects in unsafe stacks.
  • Performance appears to transfer across LLM backends, with the paper reporting roughly ±5% variation across three models, suggesting the injected relations do much of the work.
  • The modular design means OPE, URP, and the Planner could be reused in other pick-and-place pipelines or extended to navigation and manipulation tasks.
  • The SafeAgentBench result indicates that task success and safety can be balanced rather than treated as opposing objectives, at least in pick-and-place settings.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The largest reported gains are exactly where the baseline is weakest—implicit and risk-aware tasks—so a natural extension would test ConceptBot against newer grounding methods, not only against a re-implemented SayCan.
  • The hand-chosen cosine threshold and target property list are likely sensitive to the object vocabulary; unseen or niche objects would need the Wikipedia fallback and may not benefit equally.
  • A testable extension would ablate the filtering step: keep the raw ConceptNet relations but remove the 0.75 threshold, to measure how much of the gain comes from filtering versus relation injection alone.
  • The affordance score is a product of simple heuristics; replacing it with a learned affordance predictor would reveal whether the LLM or the feasibility filter contributes more to the reported margins.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

6 major / 4 minor

Summary. The paper proposes ConceptBot, a modular pick-and-place planner that injects ConceptNet relations into LLM prompts via an Object Property Extraction (OPE) module, a User Request Processing (URP) module, and an LLM-scoring Planner with affordance penalties. The authors evaluate in PyBulbit against a re-implemented 'SayCan' baseline, on self-constructed explicit, implicit, risk-aware, materials, and toxicity tasks, and also report a SafeAgentBench score. The abstract claims high success rates, generalization without fine-tuning, and validation in simulation and laboratory experiments. The main contribution is a lightweight, cache-augmented integration of a general-purpose knowledge graph with an LLM planner, with no additional training.

Significance. If the reported results are reliable, the paper would make a useful practical contribution: ConceptBot is modular, low-cost, open-sourced, and requires no fine-tuning, and the No-KG ablation suggests that ConceptNet injection has a real effect on the tested tasks. However, the evidence is not yet convincing: the SayCan comparison is against an undocumented re-implementation, the success criterion is subjective, no laboratory results are reported despite the abstract's claim, and the point estimates lack variance reporting. The architecture and ablation are a good starting point, but the evaluation needs substantial strengthening.

major comments (6)
  1. [Section V-C, Table IV] The comparison against SayCan is not a comparison against the published system. The authors re-evaluate 'SayCan' with gpt-4o-mini and adapted low-level actions because the original actions were unavailable (footnote 17). The original SayCan combines LLM relevance scores with a learned affordance value function trained on robotic experience; the reimplementation appears to drop this trained grounding, effectively reducing the baseline to an ungrounded LLM scorer. No prompt, action set, or affordance heuristic used for the baseline is provided. The reported margins (e.g., 87% vs 31% implicit; 76% vs 15% risk-aware) are therefore not evidence of an advantage over SayCan, but only over an unstated stripped baseline. Please either implement a faithful SayCan grounding (or use the released SayCan affordance scores) and report it, or rename and describe the baseline as an LLM-only baseline and
  2. [Section V-A, Section VI] The Abstract states results 'validated in both simulation and laboratory experiments,' but Section VI reports no real-world results. Section V-A describes a Franka Panda/YOLOv8/FrankaPy setup, yet no table or paragraph gives laboratory success rates, error analyses, or comparisons. This is a load-bearing validation claim. Please add the laboratory experimental results (with the same task categories and metrics) or revise the Abstract and claims to simulation-only.
  3. [Section V-C] The correctness criterion is subjective and may be biased toward ConceptBot's design. A policy is correct only if it is 'the most secure and stable one' as judged by three evaluators, and only tasks on which evaluators agree 95% are included. The 'most secure and stable' criterion aligns closely with ConceptBot's risk-averse scoring, potentially penalizing valid alternative solutions that SayCan would produce. Moreover, removing tasks that fail to reach consensus can censor exactly the cases that would discriminate fairly. Please define an objective, task-independent success definition (e.g., all safe policies satisfying the request are correct; unsafe policies are incorrect), and report all tasks attempted, including those that did not reach consensus.
  4. [Section V-C, Table IV] All reported percentages are point estimates without confidence intervals or significance tests. With ten trials per request and binary success, the standard error is large; for example a 70% success rate across ten trials has a 95% CI roughly [35%, 93%]. The headline margins are large, but the absence of variance reporting makes it impossible to tell which differences are reliable, especially in the ablation rows. Please report per-task counts and binomial confidence intervals, or per-episode metrics across trials.
  5. [Section V-B, Appendix B, Table XI] There is partial evaluation circularity: the tasks and objects appear to be selected because they have useful ConceptNet relations, and the injected relations are exactly those needed. Examples include 'jack bean RelatedTo toxic' and 'hollandaise sauce RelatedTo butter, egg, lemon'. The No-KG ablation is valuable and shows a real effect, but on such tasks the effect may be inflated by retrieval coverage; it does not establish generalization to objects absent from or sparsely covered in ConceptNet. Please add a held-out set of objects not used to choose the pipeline/tasks, report ConceptNet coverage for every object, and/or include negative cases where ConceptNet is uninformative.
  6. [Section VI, SafeAgentBench paragraph and Fig. 3] The SafeAgentBench result is reported as an 80% score versus 46% for ReAct+ThinkSafe, with virtually no protocol. The text says ConceptBot was configured with 17 low-level actions, but does not specify the prompts, the action API, how the safe/unsafe labels are integrated, or which LLM was used; Fig. 3's caption says 'using GPT-4' while Section V-A says gpt-4o-mini is used in all experiments. Without this information the external-benchmark claim is not reproducible. Please provide the full protocol, prompts, LLM version, and per-category breakdown.
minor comments (4)
  1. [Section IV-A] Typo: 'using a an hybrid' should be 'using a hybrid'.
  2. [Table II] Typo: 'canana' should be 'banana' in the Implicit task row.
  3. [Figure 4] The caption contains Italian text: 'La policy eseguita `e la seguente:' — use English for consistency.
  4. [Section IV-A] The threshold θ=0.75 is justified with examples drawn from the evaluation tasks. Please report how sensitive the final task success rates are to θ, not only the number of retrieved relations.

Circularity Check

0 steps flagged

No significant circularity: ConceptBot's KG retrieval is a mechanism, and the No-KG ablation plus the external SafeAgentBench benchmark establish that the central claim does not reduce to its inputs.

full rationale

ConceptBot's pipeline is a retrieval-augmented planning system: OPE and URP fetch ConceptNet triples, embed them, filter by cosine threshold, and inject them into the LLM prompt; the Planner scores actions by LLM votes times affordances. This is a mechanism, not a derivation, and there is no equation in which an output variable is defined as an input variable or in which a fitted parameter is renamed as a prediction. The closest concern is that the bespoke evaluation tasks (e.g., Toxicity: jack bean; Risk-Aware: aluminum tray) use objects whose relevant ConceptNet relations are exactly what OPE injects. However, the paper explicitly designs these tasks to evaluate context enrichment ('The objects contained in these prompts are also entities represented in ConceptNet, so that the advantage gained from context enrichment can be evaluated') and provides a No-KG ablation showing large drops (Implicit 87→64, Risk 76→58, Materials 70→51, Toxicity 86→56), which demonstrates the injected knowledge is doing causal work rather than the metric being definitionally tied to the output. The SafeAgentBench result (80% vs 46%) is an external benchmark independent of the authors' task design, further grounding the central claim. The main weaknesses—SayCan re-implementation with gpt-4o-mini and adapted low-level actions, and the 'most secure and stable' success criterion—are threats to comparative validity, not circular reductions of the central claim to its inputs. No self-citation chain or imported uniqueness theorem is present. The limitations section openly discusses ConceptNet gaps and URP's occasional failure to treat properties as hard constraints, which corroborates that the framework's behavior is not forced by construction.

Axiom & Free-Parameter Ledger

7 free parameters · 6 axioms · 0 invented entities

The system rests on assumptions that ConceptNet relations, embedding-similarity filtering, LLM outputs, and perception are sufficiently reliable, plus a hand-tuned threshold and property list. The evaluation adds an ad-hoc success criterion and task selection; no new physical or ontological entities are postulated. The Risk Index and property dictionaries are scoring constructs, not invented entities.

free parameters (7)
  • cosine threshold θ = 0.75
    Used in OPE and URP to filter ConceptNet relations and Wikipedia triples; chosen "based on empirical observations" on the same task suite (Section IV-A), balancing precision and recall at roughly 20 retained relations.
  • target property vocabulary = fragile, hold liquid, dangerous, safe, deformable, stable, poisonous; material sets used for materials tasks
    Hand-picked list of properties the system retains and asks the LLM to assign (Section IV-A); the list is tailored to pick-and-place safety and the evaluation tasks.
  • risk scale criteria (1-5) = Likert-style rubric in Appendix C
    Individual and interaction risk scores are defined by the authors and injected into the system prompt; they determine risk-aware task outcomes (Section IV-A, Appendix C).
  • LLM vote count n = 5
    Planner samples n=5 completions at temperature 0 and takes the majority action (Section IV-C); affects SLLM and thus plan selection.
  • affordance penalties = not numerically specified in the paper
    Sprop penalizes fragile or dangerous objects and Sbbox penalizes objects near gripper size limits; exact penalty values are not given (Section IV-C).
  • ConceptNet edge limit = 100
    Up to 100 triples are fetched per object for caching (Section IV-D); affects recall of relations.
  • fallback keyword filter list = dangerous, fragile, flammable, etc.
    When the Wikipedia fallback is active, keyword-based filtering retains only triples with these terms (Section IV-A, Appendix D).
axioms (6)
  • domain assumption ConceptNet relations are treated as correct commonsense knowledge (e.g., "jack bean" RelatedTo "toxic", "knife" IsA "sharp object").
    The OPE and URP modules inject these relations into the LLM prompt without independent verification (Section IV-A, Table XI examples).
  • domain assumption OpenAI text-embedding-ada-002 cosine similarity is a valid selector of task-relevant relations at threshold 0.75.
    Filtering relies on embedding similarity rather than semantic accuracy (Sections IV-A and IV-B).
  • domain assumption gpt-4o-mini and the other LLMs produce reliable keyword extraction, property assignment, reasoning, and action scoring when given the constructed prompts.
    All three modules depend on LLM outputs; no error analysis of LLM failures is provided (Sections IV-B, IV-C, VI).
  • domain assumption ViLD/YOLO object detection and CLIPort affordance heatmaps are accurate enough not to confound planning results.
    Object detection and grasp feasibility are inputs to planning, but perception errors are not separately evaluated (Section V-A).
  • ad hoc to paper The evaluation tasks and objects are representative of "unstructured environments" and of the claims in the abstract.
    Prompts were authored by the authors and objects selected because they are ConceptNet entities (Section V-B); no external standard benchmark is used for the main task categories.
  • ad hoc to paper A policy is correct only if it is the "most secure and stable" among valid solutions, as judged by three evaluators.
    This criterion is introduced in Section V-C and directly shapes the success rates; it favors risk-averse plans regardless of user preference.

reviewed 2026-08-05 · how reviews work

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Cite this review

Pith. "Pith review of ConceptBot: Enhancing Robot's Autonomy through Task Decomposition with Large Language Models and Knowledge Graph." pith.science (2026). https://pith.science/paper/H66ZHC7L

@misc{pith2026250900570,
  author       = {Pith},
  title        = {Pith review of: ConceptBot: Enhancing Robot's Autonomy through Task Decomposition with Large Language Models and Knowledge Graph},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/H66ZHC7L}},
  note         = {Machine review of arXiv:2509.00570}
}
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read the original abstract

ConceptBot is a modular robotic planning framework that combines Large Language Models and Knowledge Graphs to generate feasible and risk-aware plans despite ambiguities in natural language instructions and correctly analyzing the objects present in the environment - challenges that typically arise from a lack of commonsense reasoning. To do that, ConceptBot integrates (i) an Object Property Extraction (OPE) module that enriches scene understanding with semantic concepts from ConceptNet, (ii) a User Request Processing (URP) module that disambiguates and structures instructions, and (iii) a Planner that generates context-aware, feasible pick-and-place policies. In comparative evaluations against Google SayCan, ConceptBot achieved 100% success on explicit tasks, maintained 87% accuracy on implicit tasks (versus 31% for SayCan), reached 76% on risk-aware tasks (versus 15%), and outperformed SayCan in application-specific scenarios, including material classification (70% vs. 20%) and toxicity detection (86% vs. 36%). On SafeAgentBench, ConceptBot achieved an overall score of 80% (versus 46% for the next-best baseline). These results, validated in both simulation and laboratory experiments, demonstrate ConceptBot's ability to generalize without domain-specific training and to significantly improve the reliability of robotic policies in unstructured environments. Website: https://sites.google.com/view/conceptbot

Figures

Figures reproduced from arXiv: 2509.00570 by Alessandro Leanza, Angelo Moroncelli, Blerina Spahiu, Francesco Braghin, Giuseppe Vizzari, Loris Roveda.

Figure 1
Figure 1. Figure 1: ConceptBot leverages KGs and LLMs within OPE, URP, and the [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: In this example, when selecting an action, SayCan attempts to fulfill the request directly (when it understands it) by placing the food in the bowl. In [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of the harmonic mean of the models analyzed in [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Execution by ConceptBot of the prompt ‘I would like a hollandaise [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗

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Reference graph

Works this paper leans on

69 extracted references · 39 canonical work pages · cited by 2 Pith papers · 3 internal anchors

  1. [1]

    Robot planning in the real world: Research challenges and opportunities,

    R. Alterovitz, S. Koenig, and M. Likhachev, “Robot planning in the real world: Research challenges and opportunities,” Ai Magazine , vol. 37, no. 2, pp. 76–84, 2016

    work page 2016

  2. [2]

    Recent trends in task and motion planning for robotics: A survey,

    H. Guo, F. Wu, Y . Qin, R. Li, K. Li, and K. Li, “Recent trends in task and motion planning for robotics: A survey,” ACM Computing Surveys, vol. 55, no. 13s, pp. 1–36, 2023

    2023

  3. [3]

    Robotic task sequencing problem: A survey,

    S. Alatartsev, S. Stellmacher, and F. Ortmeier, “Robotic task sequencing problem: A survey,” Journal of intelligent & robotic systems , vol. 80, pp. 279–298, 2015

    work page 2015

  4. [4]

    Strips: A new approach to the application of theorem proving to problem solving,

    R. E. Fikes and N. J. Nilsson, “Strips: A new approach to the application of theorem proving to problem solving,” Artificial Intelligence, vol. 2, no. 3, pp. 189–208, 1971. [Online]. Available: https://www.sciencedirect.com/science/article/pii/0004370271900105

    arXiv 1971

  5. [5]

    Htn planning: Complexity and expressivity,

    K. Erol, J. Hendler, and D. Nau, “Htn planning: Complexity and expressivity,” Proceedings of the National Conference on Artificial Intelligence, vol. 2, 05 1994

    work page 1994

  6. [6]

    PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains

    M. Fox and D. Long, “PDDL2.1: an extension to PDDL for expressing temporal planning domains,” CoRR, vol. abs/1106.4561, 2011. [Online]. Available: http://arxiv.org/abs/1106.4561

    work page internal anchor Pith review Pith/arXiv arXiv 2011

  7. [7]

    Automated planning for robotics,

    E. Karpas and D. Magazzeni, “Automated planning for robotics,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 3, no. V olume 3, 2020, pp. 417–439, 2020. [Online]. Available: https://www.annualreviews.org/content/journals/10. 1146/annurev-control-082619-100135

    2020

  8. [8]

    Language models are few-shot learners,

    T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-V oss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amo...

  9. [9]

    Palm: scaling language modeling with pathways,

    A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann, P. Schuh, K. Shi, S. Tsvyashchenko, J. Maynez, A. Rao, P. Barnes, Y . Tay, N. Shazeer, V . Prabhakaran, E. Reif, N. Du, B. Hutchinson, R. Pope, J. Bradbury, J. Austin, M. Isard, G. Gur-Ari, P. Yin, T. Duke, A. Levskaya, S. Ghe- mawat, S. De...

    work page 2024

  10. [10]

    Scaling language models: Methods, analysis & insights from training gopher,

    J. W. Rae, S. Borgeaud, T. Cai, K. Millican, J. Hoffmann, F. Song, J. Aslanides, S. Henderson, R. Ring, S. Young, E. Rutherford, T. Hennigan, J. Menick, A. Cassirer, R. Powell, G. van den Driessche, L. A. Hendricks, M. Rauh, P.-S. Huang, A. Glaese, J. Welbl, S. Dathathri, S. Huang, J. Uesato, J. Mellor, I. Higgins, A. Creswell, N. McAleese, A. Wu, E. Else...

    Pith/arXiv arXiv 2022

  11. [11]

    Large language models still can’t plan (a benchmark for LLMs on planning and reasoning about change),

    K. Valmeekam, A. Olmo, S. Sreedharan, and S. Kambhampati, “Large language models still can’t plan (a benchmark for LLMs on planning and reasoning about change),” in NeurIPS 2022 Foundation Models for Decision Making Workshop , 2022. [Online]. Available: https://openreview.net/forum?id=wUU-7XTL5XO

    work page 2022

  12. [12]

    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. PMLR, 2022, pp. 9118–9147

    2022

  13. [13]

    On the prospects of incorporating large language models (llms) in automated planning and scheduling (aps),

    V . Pallagani, B. C. Muppasani, K. Roy, F. Fabiano, A. Loreggia, K. Murugesan, B. Srivastava, F. Rossi, L. Horesh, and A. Sheth, “On the prospects of incorporating large language models (llms) in automated planning and scheduling (aps),” in Proceedings of the International Conference on Automated Planning and Scheduling , vol. 34, 2024, pp. 432–444

    work page 2024

  14. [14]

    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, and et al, “Do as i can, not as i say: Grounding language in robotic affordances,” 2022. [Online]. Available: https://arxiv.org/abs/2204.01691

    Pith/arXiv arXiv 2022

  15. [15]

    Saycanpay: Heuristic planning with large language models using learnable domain knowledge,

    R. Hazra, P. Z. D. Martires, and L. D. Raedt, “Saycanpay: Heuristic planning with large language models using learnable domain knowledge,” 2024. [Online]. Available: https://arxiv.org/abs/2308.12682

    Pith/arXiv arXiv 2024

  16. [16]

    Grounded decoding: Guiding text generation with grounded models for embodied agents,

    W. Huang, F. Xia, D. Shah, D. Driess, A. Zeng, Y . Lu, P. Florence, I. Mordatch, S. Levine, K. Hausman et al. , “Grounded decoding: Guiding text generation with grounded models for embodied agents,” Advances in Neural Information Processing Systems , vol. 36, 2024

    work page 2024

  17. [17]

    Inner monologue: Embod- ied reasoning through planning with language models,

    W. Huang, F. Xia, T. Xiao, H. Chan, J. Liang, P. Florence, A. Zeng, J. Tompson, I. Mordatch, Y . Chebotaret al., “Inner monologue: Embod- ied reasoning through planning with language models,” in Conference on Robot Learning . PMLR, 2023, pp. 1769–1782

    work page 2023

  18. [18]

    Palm-e: An embodied multimodal language model,

    D. Driess, F. Xia, M. S. Sajjadi, C. Lynch, A. Chowdhery, B. Ichter, A. Wahid, J. Tompson, Q. Vuong, T. Yu et al., “Palm-e: An embodied multimodal language model,” arXiv preprint arXiv:2303.03378 , 2023

    Pith/arXiv arXiv 2023

  19. [19]

    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 , 2023, pp. 2998–3009

    work page 2023

  20. [20]

    Embodied multi- agent task planning from ambiguous instruction

    X. Liu, X. Li, D. Guo, S. Tan, H. Liu, and F. Sun, “Embodied multi- agent task planning from ambiguous instruction.” in Robotics: Science and Systems, 2022

    2022

  21. [21]

    Clara: classifying and disambiguating user commands for reliable interactive robotic agents,

    J. Park, S. Lim, J. Lee, S. Park, M. Chang, Y . Yu, and S. Choi, “Clara: classifying and disambiguating user commands for reliable interactive robotic agents,” IEEE Robotics and Automation Letters , 2023

    work page 2023

  22. [22]

    Survey of hallucination in natural language generation,

    Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y . Xu, E. Ishii, Y . J. Bang, A. Madotto, and P. Fung, “Survey of hallucination in natural language generation,” ACM Comput. Surv., vol. 55, no. 12, Mar. 2023. [Online]. Available: https://doi.org/10.1145/3571730

    doi:10.1145/3571730 2023

  23. [23]

    A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,

    L. Huang, W. Yu, W. Ma, W. Zhong, Z. Feng, H. Wang, Q. Chen, W. Peng, X. Feng, B. Qin, and T. Liu, “A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,” ACM Transactions on Information Systems, vol. 43, no. 2, p. 1–55, Jan. 2025. [Online]. Available: http://dx.doi.org/10.1145/3703155

    doi:10.1145/3703155 2025

  24. [24]

    Pre-trained language models for interactive decision-making,

    S. Li, X. Puig, C. Paxton, Y . Du, C. Wang, L. Fan, T. Chen, D.-A. Huang, E. Aky ¨urek, A. Anandkumar, J. Andreas, I. Mordatch, A. Torralba, and Y . Zhu, “Pre-trained language models for interactive decision-making,”

    work page

  25. [25]

    Conceptnet 5.5: An open multilingual graph of general knowledge,

    R. Speer, J. Chin, and C. Havasi, “Conceptnet 5.5: An open multilingual graph of general knowledge,” 2018. [Online]. Available: https://arxiv.org/abs/1612.03975

    Pith/arXiv arXiv 2018

  26. [26]

    Enhancing Cache-Augmented Generation (CAG) with Adaptive Contextual Compression for Scalable Knowledge Integration

    R. Agrawal and H. Kumar, “Enhancing cache-augmented generation (cag) with adaptive contextual compression for scalable knowledge integration,” 2025. [Online]. Available: https://arxiv.org/abs/2505.08261

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  27. [27]

    Ragcache: Efficient knowledge caching for retrieval-augmented generation,

    C. Jin, Z. Zhang, X. Jiang, F. Liu, X. Liu, X. Liu, and X. Jin, “Ragcache: Efficient knowledge caching for retrieval-augmented generation,” 2024. [Online]. Available: https://arxiv.org/abs/2404.12457

    Pith/arXiv arXiv 2024

  28. [28]

    Optimal structure identification with greedy search,

    D. M. Chickering, “Optimal structure identification with greedy search,” Journal of machine learning research , vol. 3, no. Nov, pp. 507–554, 2002

    work page 2002

  29. [29]

    Large language models can learn temporal reasoning,

    S. Xiong, A. Payani, R. Kompella, and F. Fekri, “Large language models can learn temporal reasoning,” arXiv preprint arXiv:2401.06853 , 2024

    Pith/arXiv arXiv 2024

  30. [30]

    Toward general-purpose robots via founda- tion models: A survey and meta-analysis,

    Y . Hu, Q. Xie, and V . Jain, “Toward general-purpose robots via founda- tion models: A survey and meta-analysis,” https://synthical.com/article/ 0b7222ba-3bb4-468f-9265-986718759f89, 11 2023

    work page 2023

  31. [31]

    Language-conditioned imitation learning for robot manipulation tasks,

    S. Stepputtis, J. Campbell, M. J. Phielipp, S. Lee, C. Baral, and H. B. Amor, “Language-conditioned imitation learning for robot manipulation tasks,” CoRR, vol. abs/2010.12083, 2020. [Online]. Available: https://arxiv.org/abs/2010.12083

    Pith/arXiv arXiv 2010

  32. [32]

    Autogpt+p: Affordance- based task planning using large language models,

    T. Birr, C. Pohl, A. Younes, and T. Asfour, “Autogpt+p: Affordance- based task planning using large language models,” in Robotics: Science and Systems XX , ser. RSS2024. Robotics: Science and Systems Foundation, Jul. 2024. [Online]. Available: http://dx.doi.org/10.15607/ RSS.2024.XX.112

    work page 2024

  33. [33]

    Open-vocabulary queryable scene representations for real world planning,

    B. Chen, F. Xia, B. Ichter, K. Rao, K. Gopalakrishnan, M. S. Ryoo, A. Stone, and D. Kappler, “Open-vocabulary queryable scene representations for real world planning,” 2022. [Online]. Available: https://arxiv.org/abs/2209.09874

    Pith/arXiv arXiv 2022

  34. [34]

    Text2motion: from natural language instructions to feasible plans,

    K. Lin, C. Agia, T. Migimatsu, M. Pavone, and J. Bohg, “Text2motion: from natural language instructions to feasible plans,” Autonomous Robots , Nov 2023. [Online]. Available: https://doi.org/10. 1007/s10514-023-10131-7

    work page 2023

  35. [35]

    Chain of thought prompting elicits reasoning in large language models,

    J. Wei, X. Wang, D. Schuurmans, M. Bosma, E. H. Chi, Q. Le, and D. Zhou, “Chain of thought prompting elicits reasoning in large language models,” CoRR, vol. abs/2201.11903, 2022. [Online]. Available: https://arxiv.org/abs/2201.11903

    Pith/arXiv arXiv 2022

  36. [36]

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

    S. Yao, D. Yu, J. Zhao, I. Shafran, T. L. Griffiths, Y . Cao, and K. Narasimhan, “Tree of thoughts: Deliberate problem solving with large language models,” 2023. [Online]. Available: https: //arxiv.org/abs/2305.10601

    Pith/arXiv arXiv 2023

  37. [37]

    Least-to- most prompting enables complex reasoning in large language models,

    D. Zhou, N. Sch ¨arli, L. Hou, J. Wei, N. Scales, X. Wang, D. Schuurmans, C. Cui, O. Bousquet, Q. Le, and E. Chi, “Least-to- most prompting enables complex reasoning in large language models,”

    work page

  38. [38]

    You only look once: Unified, real-time object detection,

    J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” 2016. [Online]. Available: https://arxiv.org/abs/1506.02640

    Pith/arXiv arXiv 2016

  39. [39]

    ViDAS: Vision-based Danger Assessment and Scoring

    P. Gupta, A. Krishnan, N. Nanda, A. Eswar, D. Agarwal, P. Gohil, and P. Goel, “Vidas: Vision-based danger assessment and scoring,” 2024. [Online]. Available: https://arxiv.org/abs/2410.00477

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  40. [40]

    The ycb object and model set: Towards common benchmarks for manipulation research,

    B. Calli, A. Singh, A. Walsman, S. Srinivasa, P. Abbeel, and A. M. Dollar, “The ycb object and model set: Towards common benchmarks for manipulation research,” in 2015 International Conference on Advanced Robotics (ICAR), 2015, pp. 510–517

    work page 2015

  41. [41]

    Benchmarking in manipulation research: Using the yale-cmu- berkeley object and model set,

    B. Calli, A. Walsman, A. Singh, S. Srinivasa, P. Abbeel, and A. M. Dollar, “Benchmarking in manipulation research: Using the yale-cmu- berkeley object and model set,” IEEE Robotics & Automation Magazine, vol. 22, no. 3, pp. 36–52, 2015

    work page 2015

  42. [42]

    Google scanned objects: A high-quality dataset of 3d scanned household items,

    L. Downs, A. Francis, N. Koenig, B. Kinman, R. Hickman, K. Reymann, T. B. McHugh, and V . Vanhoucke, “Google scanned objects: A high-quality dataset of 3d scanned household items,” 2022. [Online]. Available: https://arxiv.org/abs/2204.11918

    Pith/arXiv arXiv 2022

  43. [43]

    Open-vocabulary object detection via vision and language knowledge distillation,

    X. Gu, T.-Y . Lin, W. Kuo, and Y . Cui, “Open-vocabulary object detection via vision and language knowledge distillation,” 2022. [Online]. Available: https://arxiv.org/abs/2104.13921

    Pith/arXiv arXiv 2022

  44. [44]

    Learning transferable visual models from natural language supervision,

    A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever, “Learning transferable visual models from natural language supervision,” 2021. [Online]. Available: https://arxiv.org/abs/2103.00020

    Pith/arXiv arXiv 2021

  45. [45]

    Cliport: What and where pathways for robotic manipulation,

    M. Shridhar, L. Manuelli, and D. Fox, “Cliport: What and where pathways for robotic manipulation,” in Proceedings of the 5th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, A. Faust, D. Hsu, and G. Neumann, Eds., vol

    work page

  46. [46]

    Safeplan: Leveraging formal logic and chain- of-thought reasoning for enhanced safety in llm-based robotic task planning,

    I. Obi, V . L. N. Venkatesh, W. Wang, R. Wang, D. Suh, T. I. Amosa, W. Jo, and B.-C. Min, “Safeplan: Leveraging formal logic and chain- of-thought reasoning for enhanced safety in llm-based robotic task planning,” 2025. [Online]. Available: https://arxiv.org/abs/2503.06892

    Pith/arXiv arXiv 2025

  47. [47]

    Safe planner: Empowering safety awareness in large pre-trained models for robot task planning,

    S. Li, Z. Ma, F. Liu, J. Lu, Q. Xiao, K. Sun, L. Cui, X. Yang, P. Liu, and X. Wang, “Safe planner: Empowering safety awareness in large pre-trained models for robot task planning,” 2024. [Online]. Available: https://arxiv.org/abs/2411.06920

    Pith/arXiv arXiv 2024

  48. [48]

    Safeagentbench: A benchmark for safe task planning of embodied llm agents,

    S. Yin, X. Pang, Y . Ding, M. Chen, Y . Bi, Y . Xiong, W. Huang, Z. Xiang, J. Shao, and S. Chen, “Safeagentbench: A benchmark for safe task planning of embodied llm agents,” 2025. [Online]. Available: https://arxiv.org/abs/2412.13178

    arXiv 2025

  49. [49]

    React: Synergizing reasoning and acting in language models,

    S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y . Cao, “React: Synergizing reasoning and acting in language models,” 2023. [Online]. Available: https://arxiv.org/abs/2210.03629 APPENDIX A. Pseudo-Code of ConceptBot Algorithm 2 Pseudo-Code of ConceptBot 1: 1. Object Properties Extraction 2: O, rpn, bboxes= ObjectDetectionM odule(SceneImage ...

    Pith/arXiv arXiv 2023

  50. [54]

    Unambiguous Prompts: The complete list of prompts is provided in Table V

    work page

  51. [55]

    Ambiguous Prompts: The complete list of prompts is provided in Table VI

    work page

  52. [56]

    I MPLICIT

    Implicit Prompts: The complete list of prompts is pro- vided in Table VII. The objects listed in Table VIII are used across the Explicit and Implicit prompts. These are common household items typically found in a kitchen. SayCan is able to understand the user’s intent and the needs expressed in the prompts when there is no ambiguity involved. However, esp...

    work page

  53. [57]

    mug” is preferable over “cup

    Risk-Aware Prompts: The complete list of prompts is provided in Table B4. The evaluation of the Risk Index Prompts highlights sig- nificant limitations in SayCan’s ability to reason about risk. One of the most evident issues is its inconsistent decision- making, where correct actions are only occasionally taken. Furthermore, hallucinations occasionally oc...

    work page

  54. [58]

    For each instruction, some objects have been intentionally selected as they can mislead a standard planner

    Materials Prompts: The complete list of prompts is provided in Table X. For each instruction, some objects have been intentionally selected as they can mislead a standard planner. These ambigu- ous objects have multiple material compositions or semantic associations that might cause errors when relying solely on the instruction text. To enhance the planne...

    work page

  55. [59]

    While ConceptNet provides useful relationships to enhance the reasoning of ConceptBot, it is not specifically designed for botanical or zoological knowledge

    Toxic Prompts: The complete list of prompts is provided in Table XI. While ConceptNet provides useful relationships to enhance the reasoning of ConceptBot, it is not specifically designed for botanical or zoological knowledge. As a result, certain nodes, such as Gladiolus, may be missing, limiting the coverage of plant and animal properties. A notable lim...

    work page

  56. [60]

    The LLM utilizes semantic relationships extracted from ConceptNet to infer these properties and assign the appropriate risk level

    Individual Object Risk Assessment: Each object detected in the environment is assigned a danger score ranging from 1 to 5, based on its intrinsic properties. The LLM utilizes semantic relationships extracted from ConceptNet to infer these properties and assign the appropriate risk level. The scoring criteria are: • Score 1 - Not dangerous: The object is c...

    work page

  57. [61]

    The system identifies how an object’s risk level is affected by being placed near or interacting with another object

    Interaction-Based Risk Assessment: Beyond individual object properties, ConceptBot evaluates interactions between objects to assess compounded risks. The system identifies how an object’s risk level is affected by being placed near or interacting with another object. The scoring criteria for interactions are: • Score 1 - No added danger: The combination i...

    work page

  58. [62]

    Example Output Format: The LLM-generated risk as- sessment follows the structure: Object: plastic cup Dangerous: 1 DangerousWith: [microwave oven (3)] D. Fallback Mechanism for Knowledge Extraction When ConceptNet does not provide sufficient knowledge about an object, ConceptBot can be implemented with a fall- back mechanism to retrieve additional context...

    work page

  59. [63]

    However, Wikipedia often returns multiple relevant pages for a single query

    Querying Wikipedia: The detected object name is used as a query through the Wikipedia API. However, Wikipedia often returns multiple relevant pages for a single query

    work page

  60. [64]

    Page Selection: the system prompts the user to select the correct page from the retrieved options

    work page

  61. [65]

    Extracting Textual Content:Once a relevant Wikipedia page is identified, its textual content is extracted

    work page

  62. [66]

    Applying Open Information Extraction (OpenIE): The extracted text is processed using OpenIE to identify structured triples of the form (subject, relation, object) , similar to ConceptNet’s representation

    work page

  63. [67]

    dangerous,

    Filtering Relations: Since Wikipedia pages contain a large number of extracted triples, a filtering process is necessary: • Keyword-based filtering: Only relations containing relevant terms (e.g., “dangerous, ” “fragile, ” “toxic, ” “flammable”) are retained. This method is compu- tationally efficient and effective for selecting mean- ingful relationships...

    work page

  64. [68]

    This allows ConceptBot to use Wikipedia- derived knowledge in the same way as ConceptNet data

    Integration with OPE: The filtered relationships are structured similarly to ConceptNet relations and incor- porated into the Object Properties Extraction (OPE) module. This allows ConceptBot to use Wikipedia- derived knowledge in the same way as ConceptNet data

    work page

  65. [69]

    • High Number of Extracted Triples: OpenIE often generates a large number of triples, increasing filtering complexity and computational cost

    Fallback Mechanism: Limitations and Future Improve- ments: While the fallback mechanism increases ConceptBot’s adaptability, it introduces certain challenges: • Page Selection Ambiguity: In cases where multiple Wikipedia pages match an object name, automatic se- lection remains a challenge. • High Number of Extracted Triples: OpenIE often generates a larg...

    work page

  66. [164]

    PMLR, 08–11 Nov 2022, pp. 894–906. [Online]. Available: https://proceedings.mlr.press/v164/shridhar22a.html

    work page 2022

  67. [2020]

    Available: https://arxiv.org/abs/2005.14165

    [Online]. Available: https://arxiv.org/abs/2005.14165

    Pith/arXiv arXiv 2005

  68. [2022]

    Available: https://arxiv.org/abs/2202.01771

    [Online]. Available: https://arxiv.org/abs/2202.01771

    Pith/arXiv arXiv

  69. [2023]

    Available: https://arxiv.org/abs/2205.10625

    [Online]. Available: https://arxiv.org/abs/2205.10625

    Pith/arXiv arXiv

This paper was first reviewed by deepseek-v4-flash on August 5, 2026.