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REVIEW 3 major objections 5 minor 68 references

A Hierarchical Approach to Imitation Learning for Manipulation Tasks Requiring Time Varying Forces

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The paper's central claim is that decoupling task selection from force-reactive trajectory generation lets robots separate bonded sheets safely, where standard diffusion policies fail.

desk verdict A genuinely plausible slow-fast architecture for force-reactive imitation learning, but the evaluation is too thin and too reliant on a magnetic adhesive surrogate to support the transfer claims. read the letter →

arxiv 2608.03103 v1 pith:SG3JW7VW submitted 2026-08-04 cs.RO cs.AI

Rishabh Shukla , Adithya Santhosh , Shaili Gandhi , Samrudh Moode , Satyandra K. Gupta This is my paper · ORCID
classification cs.ROcs.AI
keywords diffusionpolicyimitationlearningcontact-richmanipulationforcefeedbackhierarchicalcontrolbimanuallatentprimitivecompliantsheetseparation
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

The paper argues that the bottleneck for diffusion policies in contact-rich material separation is the open loop between replans: a pre-planned action chunk cannot react to force spikes during fracture. Its solution, DPA-FTG, decouples a slow 5 Hz diffusion planner that selects a discrete latent skill from a fast 60 Hz force-conditioned recurrent decoder that generates velocity commands. On a bimanual bonded-sheet separation test, the full system reaches 100% episode completion and 100% task success in-distribution with mean peak force 27.7 N, while the closest baseline completes episodes but is unsafe, with 33.3% task success and 44.7 N peak force. A sympathetic reading is that fast closed-loop force regulation, not faster generation, is what makes diffusion policies viable for stiff-contact manipulation.

What carries the argument

The central object is the latent primitive: a vector in a learned discrete codebook, produced by a VQ-VAE, that encodes a motion strategy such as 'oscillate forward' or 'pry upward' rather than a sequence of poses. It carries the argument by being the stable interface between two timescales: the 5 Hz diffusion planner emits a primitive, and the 60 Hz GRU decoder reads it from a shared atomic buffer and modulates execution with instantaneous force and proprioception. Three design choices make the mechanism work: zero-initialized sensor inputs preserve the pre-trained kinematic behavior at the start of fine-tuning; denoising the fast inputs during training teaches an implicit admittance-like response; and quantizing the diffusion output to the nearest codebook vector prevents mode-averaged, physically invalid intents.

What would settle it

A direct transfer experiment: train on demonstrations of the same sheet separation performed on a chemically bonded workpiece instead of the magnet fixture, then compare DPA-FTG against the reactive baseline on that real bond with matched peel resistance. If DPA-FTG's task-success margin over the baseline does not reproduce, or peak forces exceed the safety threshold on the real bond, the surrogate evidence does not establish the central claim.

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Extended reading notes

Core claim

DPA-FTG moves action chunking from the action space to a latent task space. A VQ-VAE compresses demonstration trajectory windows of joint velocities and contact forces into a discrete codebook of latent primitives; a conditional diffusion model samples one of these primitives at 5 Hz from vision, force history, and proprioception; and a GRU decoder, initialized from the VQ-VAE decoder and fine-tuned with force injection and denoising, unrolls the primitive into 60 Hz velocity commands. The paper reports in-distribution 100% task success at 0.85x human speed and 27.7 N mean peak force, versus 33.3% task success and 44.7 N peak force for the reactive baseline, with zero-shot task success of 80% on an interpolated hexagon and 60% on an extrapolated triangle. It also identifies two generalization failure modes: latent mismatch at acute corners and compounding drift after tool slip.

Load-bearing premise

The load-bearing premise is that the 16-magnet surrogate faithfully preserves the control-relevant force transients, such as stick-slip and intermittent release, of real adhesive bonds; if it does not, the reported success may not transfer to the motivating battery or sealant-removal tasks.

Editorial extensions

If this is right

  • A 5 Hz diffusion planner can drive 60 Hz interaction safely if the chunk is a stable intent rather than a pose sequence.
  • Discrete latent skills are load-bearing: removing VQ quantization lowers task success from 100% to 60%, consistent with mode-averaging that produces invalid trajectories.
  • Fast-loop force feedback is the main safety mechanism: removing it raises mean peak force from 27.7 N to 34.5 N and lowers task success from 100% to 73.3%.
  • Open-loop action chunks, as in the force-conditioned diffusion policy and standard diffusion policy, degrade sharply on unseen geometries, while the hierarchical system retains 60-80% task success.
  • The reported failure modes point to the next bottleneck: the planner's geometric perception and the absence of an explicit recovery skill, rather than the 60 Hz controller itself.

Reading between the lines

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

  • Because validation uses a magnetic surrogate rather than a real chemical adhesive, the 27.7 N peak-force figure should be re-measured on a bonded sheet with similar peel resistance; the transfer margin may shrink if the real bond exhibits rate-dependent stick-slip that the magnets do not reproduce.
  • The same slow-intent and fast-force decomposition should transfer to other force-modulation skills such as scraping corrosion, prying, or polishing, where the strategy is semantic and the execution is impedance-like.
  • Adding an explicit recovery primitive for tool-edge slip could address the compounding-drift abort without changing the architecture, since the observed failure was the absence of a matching skill in the vocabulary.
  • The latent-mismatch failures at acute corners might be reduced by learning the primitive vocabulary from unlabeled demonstrations rather than with a fixed codebook, a testable extension the paper leaves for future work.
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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

3 major / 5 minor

Summary. The manuscript proposes DPA-FTG, a hierarchical imitation-learning architecture for contact-rich manipulation. A 5 Hz conditional diffusion model selects a discrete latent primitive from a VQ-VAE codebook learned on expert demonstrations, and a 60 Hz GRU-based decoder—initialized from the VQ-VAE decoder and fine-tuned with force and proprioceptive inputs—generates closed-loop joint-velocity commands. The method is evaluated on a bimanual compliant-sheet separation task using a magnetic surrogate for adhesive bonds, reporting 100% task success and a mean peak force of 27.7 N versus RDP's 33.3% and 44.7 N, together with ablations and zero-shot tests on two unseen geometries.

Significance. The central idea—moving action chunking from the action space to a latent task-primitive space so that a fast force-reactive loop can run at 60 Hz—is timely, well motivated, and technically coherent. The ablations isolate the contributions of task selection, force feedback, VQ quantization, and decoder weight transfer, and the failure-mode analysis is thoughtful. The experiments are conducted on real hardware. However, the empirical support for the headline safety claim is thin: 15 in-distribution and 10 zero-shot trials per condition, no confidence intervals or significance tests, and a single unblinded evaluator for the ordinal execution-quality score. The surrogate magnetic fixture, which the authors acknowledge does not reproduce the full constitutive behavior of chemical adhesives, further limits the transferability of the reported margins to real battery disassembly or sealant removal. These issues are load-bearing for the paper's central claim, so the manuscript requires substantial revision.

major comments (3)
  1. [5.1.1, Tables 2–4] The magnetic surrogate workpiece is the sole physical validation platform, and the authors state that it is not intended to reproduce the full constitutive behavior of a specific chemical adhesive, abstracting away rate dependence, temperature effects, and spatially heterogeneous cure states. Because the 60 Hz force-reactive controller is learned and evaluated on magnet-disengagement force transients, the reported 100% versus 33.3% task-success margin and the 27.7 N versus 44.7 N peak-force advantage may not transfer to the motivating real adhesive or sealant applications. Please provide either a small validation on a real adhesive/sealant, or quantitative evidence (e.g., example force traces and transient statistics) that the surrogate preserves the control-relevant features, and correspondingly temper the application-level conclusions.
  2. [5.3, 5.5] Task Success is derived from Execution Quality, an ordinal score assigned by the same unblinded evaluator and incorporating subjective criteria such as gouging and slips. With only 15 in-distribution trials per method and 5 trials per unseen geometry, and with no confidence intervals or significance tests reported, the large margins (100% vs 33.3%) are not statistically substantiated. Please report bootstrap confidence intervals and a significance test (e.g., Fisher's exact test for Task Success and a bootstrap test for peak force), and consider using multiple blinded evaluators or a fully objective safety metric.
  3. [5.4, Table 2] The BC baseline is reported as N/A because it consistently triggered safety stops in preliminary rollouts, meaning the comparison set is incomplete. The paper's implication that non-generative baselines are inadequate for this task is therefore not empirically demonstrated. Please provide a functioning BC baseline (even with low success, perhaps with a safety wrapper or additional training) or explicitly remove the BC comparison and the associated conclusion.
minor comments (5)
  1. [4.2, 4.3, 4.4] The manuscript does not report important hyperparameters—codebook size K, latent dimension D_z, GRU hidden size, number of diffusion steps, denoising noise magnitudes, and the loss weights lambda and beta in Eq. (9). A full implementation-details table is needed for reproducibility.
  2. [Abstract] The phrase 'makes high frequency control difficult' should be 'making high-frequency control difficult' for grammatical correctness.
  3. [5.3] The Execution Quality thresholds of 30 N and 40 N are described as conservative, but no physical justification is given; please provide a rationale based on substrate yield force, tool limits, or hardware specifications.
  4. [4.4] The term 'Denoising Autoencoder for Dynamics' is potentially confusing because noise is injected into the inputs (F, q, q_dot) rather than into the action labels; consider renaming this to something like 'input-perturbation training' to avoid implying a standard denoising objective.
  5. [5.5] The statement that performance margins 'remain consistent across all experimental axes' is presented as a robustness claim but is not supported by quantitative variance measures or statistical tests; please add such measures or qualify the statement.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: DPA-FTG's latent-code training and force-reactive decoding are evaluated on physical outcomes, not on the model's own outputs; self-citations are background only.

full rationale

The paper's derivation chain is self-contained. The high-level planner (Sec. 4.3) is trained with a DDPM noise-prediction objective whose clean target is the codebook vector e_{nk} produced by the VQ-VAE encoder on expert trajectories, and at inference it quantizes its continuous sample to the nearest codebook entry via Eq. (11). The low-level policy (Sec. 4.4) is initialized from the VQ-VAE decoder and fine-tuned with the action-reconstruction loss in Eq. (13), supervising clean expert actions while injecting noise into force and proprioceptive inputs. These are standard supervised-learning steps and do not make the evaluation metric an input: Episode Completion, Task Success, Execution Quality, and peak force (Sec. 5.3) are measured on new physical trials, with Task Success defined by force thresholds and video review rather than by the predicted latent codes. The ablations (Table 3) and baseline comparisons (Tables 1, 2, and 4) are executed experiments; the RDP baseline, the VQ-VAE, and diffusion formulas are external or independently implemented. Self-citations ([1], [8], [50]) provide background or baselines but do not bear the central claim. The stated limitation of the magnetic surrogate (Sec. 5.1.1) is an external-validity risk, not a circular step: the fixture abstracts away rate dependence, temperature, and heterogeneous cure states, so transfer to real adhesives is not demonstrated; likewise the single-evaluator execution-quality scoring is a measurement-bias risk. Neither makes the derivation equivalent to its inputs.

Assumptions & free parameters 3 free parameters · 4 assumptions · 1 invented entities

The central claim rests on the surrogate task validity, demonstration sufficiency, and two architectural bets: zero-init transfer and VQ quantization. The policy weights themselves are fit to demonstrations, which is standard; the free parameters are mostly unreported hyperparameters and hand-set safety thresholds.

free parameters (3)
  • VQ-VAE codebook size K and latent dimension D_z = not reported
    The number of discrete skill primitives and their embedding dimension shape the latent vocabulary that interfaces the two policy levels. The paper does not state these values or justify their selection.
  • Safety thresholds for Execution Quality = 30 N and 40 N peak chisel force
    Hand-chosen thresholds define the ordinal quality scores and therefore the Task Success metric. They are not derived from any model, standard, or analysis.
  • Denoising augmentation noise magnitudes = not reported
    The injected sensor noise in Eq. 13 determines the strength of the implicit admittance behavior during fine-tuning, but the noise levels are not specified.
assumptions (4)
  • domain assumption The 16-magnet surrogate fixture captures the control-relevant dynamics of real adhesive fracture, including stick-slip and rapid force transients.
    Section 5.1.1 states the setup is not intended to reproduce the full constitutive behavior of a chemical adhesive but claims it preserves the key control-relevant challenge. Transfer to real adhesive bonds is assumed.
  • domain assumption 51 MoCap demonstrations by human experts are sufficient to learn a generalizable latent primitive vocabulary and policy.
    Data collection in Sec. 5.2 uses 17 demonstrations per training geometry with no analysis of coverage, diversity, or data efficiency.
  • ad hoc to paper Zero-initialized new input channels keep the fine-tuned GRU close to the pre-trained decoder so kinematic skills are preserved during reactivity fine-tuning.
    Sec. 4.4 introduces this as a design strategy. Its success is only validated empirically by the w/o Weight Transfer ablation, not by any formal guarantee.
  • standard math The DDPM objective and nearest-neighbor quantization in Eqs. 10 and 11 produce valid skill selections from the continuous diffusion output.
    This relies on standard diffusion model assumptions; the paper does not prove that quantization preserves the semantic content of the predicted embedding.
invented entities (1)
  • Latent primitive vocabulary Z (VQ-VAE codebook)
    purpose: Compact discrete interface between the 5 Hz task selector and the 60 Hz force-reactive decoder, encoding skills such as oscillate forward and pry upward.
    The vocabulary is learned from the paper's own 51 demonstrations and only validated within its experiments. There is no external benchmark or independent measurement showing these codes correspond to real task semantics.

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Pith. "Pith review of A Hierarchical Approach to Imitation Learning for Manipulation Tasks Requiring Time Varying Forces." pith.science (2026). https://pith.science/paper/SG3JW7VW

@misc{pith2026260803103,
  author       = {Pith},
  title        = {Pith review of: A Hierarchical Approach to Imitation Learning for Manipulation Tasks Requiring Time Varying Forces},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SG3JW7VW}},
  note         = {Machine review of arXiv:2608.03103}
}
abstract

Diffusion policies have shown strong performance in learning complex, multi-modal behaviors for robotic manipulation. However, their application to contact-rich disassembly tasks remains limited by a key trade-off: the iterative denoising process introduces inference latencies that makes high frequency control difficult, which is essential for realizing dynamic interactions such as chiseling and prying. Recent action-chunking techniques mitigate latency but use an open-loop execution window, rendering the system blind to rapid force transients caused by fracture events. To bridge this gap, we introduce the Diffusion Policy Augmented by Fast Trajectory Generation (DPA-FTG). Compared to recent visual-tactile approaches that focus on positional correction, DPA-FTG decouples low-frequency planning from high-frequency force regulation. At the high level ($5$ Hz), a conditional diffusion model predicts a sequence of latent parameters for selecting a strategy from a learned vocabulary of task primitives. At the low level ($60$ Hz), a lightweight, force-conditioned policy acts as a neural impedance controller, modulating execution in real-time to maintain contact stability. We validate our approach on a bimanual battery disassembly task involving the separation of a compliant sheet. Experimental evaluation demonstrates that DPA-FTG outperforms state-of-the-art baselines, including Reactive Diffusion Policy (RDP).

Figures

Figures reproduced from arXiv: 2608.03103 by the authors.

Figure 1
Figure 1. Material separation tasks require complex force modulation across diverse domains. (A) [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. System Architecture of DPA-FTG. The framework decouples high-level task planning from low-level reactive control. (A) Slow Loop (5 Hz): The Task Selection Policy (πtask) takes a history of multi-modal observations (Vision, Force, Proprioception) to predict a continuous latent embedding z˜. This embedding is quantized to the nearest codebook vector zk and published to an atomic buffer. (B) Fast Loop (60 Hz): The Moti… view at source ↗
Figure 3
Figure 3. Architecture for Latent Learning and Policy Transfer. (A) Skill Discovery (VQ-VAE): During pre-training, the encoder compresses expert trajectories (Action + Force) into a discrete latent code zq. The decoder is trained to reconstruct the action profile aˆ, with an auxiliary head reconstructing the force profile ˆf to ensure the latent captures dynamic context. (B) Policy Fine￾Tuning: The low-level controller (πmoti… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Asynchronous Inference Timing. The system enables high-frequency control despite the computational latency of the diffusion planner. (A) Planner Thread: The diffusion process takes ≈ 200 ms to denoise and predict the next target skill zk. (B) Control Thread: Executing …
Figure 5
Figure 5. Figure 5: The bimanual robotic cell used for experimental validation. The left arm is equipped with [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]
Figure 6
Figure 6. Figure 6: Surrogate Workpiece Design and Magnetic Adhesion Mechanism. The custom fixture models a battery module with a compliant aluminum cover. (A) Adhesion is provided by an array of 16 neodymium magnets. (B) Upon separation, magnets retract to simulate bond fracture. (C) A l…
Figure 7
Figure 7. Figure 7: Tools and Sensors. (Top) Close-up views of the custom-designed gripper and chisel. The gripper is based on the UMI-Gripper design [7]. (Bottom) The sensor suite comprises of two global RealSense D415 depth camera, an in-hand RealSense D405 depth camera, and an ATI Forc…
Figure 8
Figure 8. Figure 8: Expert Demonstration Setup. A human operator performs the bimanual compliant sheet separation task on a surrogate workpiece. This data collection method allows for the capture of natural manipulation dynamics, specifically the high-frequency oscillatory primitives requ…
Figure 9
Figure 9. Figure 9: Geometric Generalization Set. The five distinct compliant sheet geometries used to evaluate the system. The Octagon, Square, and Circle serve as the training set. The Hexagon and Triangle are reserved as unseen test samples to evaluate the policy’s zero-shot generaliza…
Figure 10
Figure 10. Figure 10: A detailed breakdown of a human expert demonstration for the bimanual compliant [PITH_FULL_IMAGE:figures/full_fig_p017_10.png]
Figure 11
Figure 11. Figure 11: Failure Mode Analysis. Illustration of two distinct failure modes during the task. (A) Latent Mismatch (Unsafe Completion): The system executes an incorrect skill, Prying, instead of the intended Cornering sequence. This results in an Unsafe High-Force pry action on t…

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Works this paper leans on

68 extracted references · 45 canonical work pages

  1. [1]

    Shukla, R

    R. Shukla, R. Talan, S. Moode, N. Dhanaraj, J. H. Kang, and S. K. Gupta. Force-conditioned diffusion policies for compliant sheet separation tasks in bimanual robotic cells. In2025 IEEE International Conference on Robotics and Automation (ICRA), pages 7960–7966, 2025. doi: 10.1109/ICRA55743.2025.11127816

    arXiv 2025

  2. [2]

    I. Kay, S. Farhad, A. Mahajan, R. Esmaeeli, and S. R. Hashemi. Robotic disassembly of electric vehicles’ battery modules for recycling.Energies, 15(13), 2022. ISSN 1996-1073. doi:10.3390/en15134856. URLhttps://www.mdpi.com/1996-1073/15/13/4856

    doi:10.3390/en15134856 2022

  3. [3]

    J. F. Hellmuth, N. M. DiFilippo, and M. K. Jouaneh. Assessment of the automation potential of electric vehicle battery disassembly.Journal of Manufacturing Systems, 59:398–412, 2021

    work page 2021

  4. [4]

    Hussein, M

    A. Hussein, M. M. Gaber, E. Elyan, and C. Jayne. Imitation learning: A survey of learning methods.ACM Computing Surveys (CSUR), 50(2):1–35, 2017

    2017

  5. [5]

    B. Fang, S. Jia, D. Guo, M. Xu, S. Wen, and F. Sun. Survey of imitation learning for robotic ma- nipulation.International Journal of Intelligent Robotics and Applications, 3:362–369, 2019

    work page 2019

  6. [6]

    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, page 02783649241273668, 2023

    2023

  7. [7]

    C. Chi, Z. Xu, C. Pan, E. Cousineau, B. Burchfiel, S. Feng, R. Tedrake, and S. Song. Universal manipulation interface: In-the-wild robot teaching without in-the-wild robots. 07 2024. doi: 10.15607/RSS.2024.XX.045

    doi:10.15607/rss.2024.xx.045 2024

  8. [8]

    J. H. Kang, S. Joshi, R. Huang, and S. K. Gupta. Robotic compliant object prying using diffusion policy guided by vision and force observations.IEEE Robotics and Automation Letters, 10(6):5505–5512, 2025. doi:10.1109/LRA.2025.3553689

    work page arXiv 2025

Show all 68 references
  1. [9]

    Florence, C

    P. Florence, C. Lynch, A. Zeng, O. A. Ramirez, A. Wahid, L. Downs, A. Wong, J. Lee, I. Mor- datch, and J. Tompson. Implicit behavioral cloning. InConference on Robot Learning, pages 158–168. PMLR, 2022

    2022

  2. [10]

    H. Xue, J. Ren, W. Chen, G. Zhang, F. Yuan, G. Gu, H. Xu, and C. Lu. Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation. volume 21, June 2025. ISBN 979-8-9902848-1-4. URLhttps://www.roboticsproceedings.org/ rss21/p052.html

    2025

  3. [11]

    J. Ho, A. Jain, and P. Abbeel. Denoising diffusion probabilistic models. InProceedings of the 34th International Conference on Neural Information Processing Systems, NIPS ’20, pages 6840–6851, Red Hook, NY , USA, Dec. 2020. Curran Associates Inc. ISBN 978-1-7138-2954- 6

    2020

  4. [12]

    Mandlekar, D

    A. Mandlekar, D. Xu, J. Wong, S. Nasiriany, C. Wang, R. Kulkarni, L. Fei-Fei, S. Savarese, Y . Zhu, and R. Martín-Martín. What matters in learning from offline human demonstrations for robot manipulation. In A. Faust, D. Hsu, and G. Neumann, editors,Proceedings of the 22 5th C...

    2022

  5. [13]

    R. Wolf, Y . Shi, S. Liu, and R. Rayyes. Diffusion models for robotic manipulation: a survey. Frontiers in Robotics and AI, V olume 12 - 2025, 2025. ISSN 2296-9144. doi:10.3389/frobt. 2025.1606247. URLhttps://www.frontiersin.org/journals/robotics-and-ai/ articles/10.3389/frobt...

    2025

  6. [14]

    Y . Ze, G. Zhang, K. Zhang, C. Hu, M. Wang, and H. Xu. 3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations. volume 20, July 2024. ISBN 979- 8-9902848-0-7. URLhttps://www.roboticsproceedings.org/rss20/p067.html

    2024

  7. [15]

    J. Wen, Y . Zhu, M. Zhu, Z. Tang, J. Li, Z. Zhou, X. Liu, C. Shen, Y . Peng, and F. Feng. DiffusionVLA: Scaling Robot Foundation Models via Unified Diffusion and Autoregression. June 2025. URLhttps://openreview.net/forum?id=VdwdU81Uzy

    2025

  8. [16]

    H. Deng, W. Guo, Q. Wang, Z. Wu, and Z. Wang. Safebimanual: Diffusion-based trajectory optimization for safe bimanual manipulation. In J. Lim, S. Song, and H.-W. Park, editors, Proceedings of The 9th Conference on Robot Learning, volume 305 ofProceedings of Machine Learning Re...

    2025

  9. [17]

    Batra and G

    S. Batra and G. Sukhatme. Zero-Shot Visual Generalization in Robot Manipulation.arXiv e-prints, art. arXiv:2505.11719, May 2025. doi:10.48550/arXiv.2505.11719

    2025 doi arXiv

  10. [18]

    Cao et al

    J. Cao et al. Mamba policy: Towards efficient 3d diffusion policy with hybrid selective state models. InProc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025. arXiv:2409.07163

    2025 arXiv

  11. [19]

    Tsuji, Y

    T. Tsuji, Y . Kato, G. Solak, H. Zhang, T. Petri ˇc, F. Nori, and A. Ajoudani. A Survey on Imitation Learning for Contact-Rich Tasks in Robotics.arXiv e-prints, art. arXiv:2506.13498, June 2025. doi:10.48550/arXiv.2506.13498

    2025 doi arXiv

  12. [20]

    Z. Li, A. Chapin, E. Xiang, R. Yang, B. Machado, N. Lei, E. Dellandrea, D. Huang, and L. Chen. Robotic manipulation via imitation learning: Taxonomy, evolution, benchmark, and challenges.arXiv preprint arXiv:2508.17449, 2025

    2025 arXiv

  13. [21]

    U. A. Mishra, Y . Chen, and D. Xu. Generative factor chaining: Coordinated manipulation with diffusion-based factor graph. In P. Agrawal, O. Kroemer, and W. Burgard, editors,Proceedings of The 8th Conference on Robot Learning, volume 270 ofProceedings of Machine Learning Resea...

    2025

  14. [22]

    A. Z. Ren, J. Lidard, L. L. Ankile, A. Simeonov, P. Agrawal, A. Majumdar, B. Burchfiel, H. Dai, and M. Simchowitz. Diffusion policy policy optimization. InThe Thirteenth Inter- national Conference on Learning Representations, 2025. URLhttps://openreview.net/ forum?id=mEpqHvbD2h

    2025

  15. [23]

    Janner, Y

    M. Janner, Y . Du, J. Tenenbaum, and S. Levine. Planning with diffusion for flexible behavior synthesis. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, and S. Sabato, edi- tors,Proceedings of the 39th International Conference on Machine Learning, volume 162 of Pr...

    2022

  16. [24]

    J. Song, C. Meng, and S. Ermon. Denoising diffusion implicit models. InInternational Con- ference on Learning Representations, 2021. URLhttps://openreview.net/forum?id= St1giarCHLP

    2021

  17. [25]

    Y . Song, P. Dhariwal, M. Chen, and I. Sutskever. Consistency models. In A. Krause, E. Brun- skill, K. Cho, B. Engelhardt, S. Sabato, and J. Scarlett, editors,Proceedings of the 40th Inter- national Conference on Machine Learning, volume 202 ofProceedings of Machine Learning R...

    2023

  18. [26]

    G. Yan, J. Zhu, Y . Deng, S. Yang, R.-Z. Qiu, X. Cheng, M. Memmel, R. Krishna, A. Goyal, X. Wang, and D. Fox. Maniflow: A general robot manipulation policy via consistency flow training. In J. Lim, S. Song, and H.-W. Park, editors,Proceedings of The 9th Conference on Robot Lea...

    2025

  19. [27]

    G. Lu, Z. Gao, T. Chen, W. Dai, Z. Wang, and Y . Tang. Manicm: Real-time 3d diffusion policy via consistency model for robotic manipulation.CoRR, abs/2406.01586, 2024. URL https://doi.org/10.48550/arXiv.2406.01586

    2024 doi arXiv

  20. [28]

    Prasad, K

    A. Prasad, K. Lin, J. Wu, L. Zhou, and J. Bohg. Consistency Policy: Accelerated Visuomotor Policies via Consistency Distillation. InRobotics: Science and Systems XX. Robotics: Science and Systems Foundation, July 2024. ISBN 979-8-9902848-0-7. doi:10.15607/RSS.2024.XX

    2024 doi

  21. [29]

    K. Lei, H. Li, D. Yu, Z. Wei, L. Guo, Z. Jiang, Z. Wang, S. Liang, and H. Xu. RL-100: Performant Robotic Manipulation with Real-World Reinforcement Learning.arXiv e-prints, art. arXiv:2510.14830, Oct. 2025. doi:10.48550/arXiv.2510.14830

    2025 doi

  22. [30]

    Y . Wu, H. Wang, Z. Chen, J. Pang, and D. Xu. On-device diffusion transformer policy for efficient robot manipulation. InProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025. arXiv:2508.00697

    2025 arXiv

  23. [31]

    Zhang, Z

    R. Zhang, Z. Luo, J. Sjölund, P. Mattsson, L. Gisslén, and A. Sestini. Real-Time Diffusion Policies for Games: Enhancing Consistency Policies with Q-Ensembles. July 2025. URL https://openreview.net/forum?id=pOrMV8hDrk

    2025

  24. [32]

    Y . Duan, H. Yin, and D. Kragic. Real-time iteration scheme for diffusion policy, 2025. URL https://arxiv.org/abs/2508.05396

    2025 arXiv

  25. [33]

    Clemente, L

    M. Clemente, L. Brunswic, R. H. Yang, et al. Two-steps diffusion policy for robotic manip- ulation via genetic denoising. InProceedings of the Neural Information Processing Systems (NeurIPS), 2025. arXiv:2510.21991

    2025

  26. [34]

    Y . Niu, S. Zhou, Y . Li, Y . Den, and L. Wang. Time-Unified Diffusion Policy with Action Discrimination for Robotic Manipulation, June 2025. URLhttp://arxiv.org/abs/2506. 09422. arXiv:2506.09422 [cs]

    2025 arXiv

  27. [35]

    Hegde, G

    S. Hegde, G. Salhotra, and G. S. Sukhatme. Latent weight diffusion: Generating policies from trajectories.CoRR, abs/2410.14040, 2024. URLhttps://doi.org/10.48550/arXiv. 2410.14040

    2024 doi

  28. [36]

    X. Ye, R. H. Yang, J. Jin, Y . Li, and A. Rasouli. RA-DP: Rapid Adaptive Diffusion Policy for Training-Free High-frequency Robotics Replanning, July 2025. URLhttp://arxiv.org/ abs/2503.04051. arXiv:2503.04051 [cs]

    2025 arXiv

  29. [37]

    J. Jia, T. Yang, X. Chen, C. Liu, and W. Zhang. Fast Visuomotor Policy for Robotic Manipu- lation.arXiv e-prints, art. arXiv:2510.12483, Oct. 2025. doi:10.48550/arXiv.2510.12483

    2025 doi

  30. [38]

    Zhang, Z

    R. Zhang, Z. Luo, J. Sjölund, P. Mattsson, L. Gisslén, and A. Sestini. Real-time diffusion policies for games: Enhancing consistency policies with q-ensembles, 2025. URLhttps: //arxiv.org/abs/2503.16978

    2025 arXiv

  31. [39]

    Smith, Y

    C. Smith, Y . Karayiannidis, L. Nalpantidis, X. Gratal, P. Qi, D. V . Dimarogonas, and D. Kragic. Dual arm manipulation—a survey.Robotics and Autonomous systems, 60(10):1340–1353, 2012

    2012

  32. [40]

    Stepputtis, M

    S. Stepputtis, M. Bandari, S. Schaal, and H. B. Amor. A system for imitation learning of contact-rich bimanual manipulation policies. In2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 11810–11817. IEEE, 2022

    2022

  33. [41]

    Y . Chen, T. Wu, S. Wang, X. Feng, J. Jiang, Z. Lu, S. McAleer, H. Dong, S.-C. Zhu, and Y . Yang. Towards human-level bimanual dexterous manipulation with reinforcement learning. Advances in Neural Information Processing Systems, 35:5150–5163, 2022. 24

    2022

  34. [42]

    X. Ma, S. Patidar, I. Haughton, and S. James. Hierarchical Diffusion Policy for Kinematics- Aware Multi-Task Robotic Manipulation. In2024 IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition (CVPR), pages 18081–18090, Seattle, W A, USA, June 2024. IEEE. ISBN 979-8-...

    2024

  35. [43]

    Zhang, Y

    J. Zhang, Y . Guo, X. Chen, Y .-J. Wang, Y . Hu, C. Shi, and J. Chen. HiRT: Enhancing Robotic Control with Hierarchical Robot Transformers. InProceedings of The 8th Conference on Robot Learning, pages 933–946. PMLR, Jan. 2025. URLhttps://proceedings.mlr.press/ v270/zhang25b.ht...

    2025

  36. [44]

    N. Hogan. Impedance Control: An Approach to Manipulation. In1984 American Control Conference, pages 304–313, June 1984. doi:10.23919/ACC.1984.4788393. URLhttps: //ieeexplore.ieee.org/document/4788393

    1984

  37. [45]

    M. H. Raibert and J. J. Craig. Hybrid Position/Force Control of Manipulators.Journal of Dynamic Systems, Measurement, and Control, 103(2):126–133, June 1981. ISSN 0022-0434. doi:10.1115/1.3139652. URLhttps://doi.org/10.1115/1.3139652

    1981 doi

  38. [46]

    A. J. Ijspeert, J. Nakanishi, H. Hoffmann, P. Pastor, and S. Schaal. Dynamical movement primitives: learning attractor models for motor behaviors.Neural computation, 25(2):328– 373, 2013

    2013

  39. [47]

    Y . Lin, A. Church, M. Yang, H. Li, J. Lloyd, D. Zhang, and N. F. Lepora. Bi-touch: Bi- manual tactile manipulation with sim-to-real deep reinforcement learning.IEEE Robotics and Automation Letters, 2023

    2023

  40. [48]

    Helmut, N

    E. Helmut, N. Funk, T. Schneider, C. de Farias, and J. Peters. Tactile-Conditioned Diffusion Policy for Force-Aware Robotic Manipulation.arXiv e-prints, art. arXiv:2510.13324, Oct

  41. [49]

    K. Yu, Y . Han, Q. Wang, V . Saxena, D. Xu, and Y . Zhao. Mimictouch: Leveraging multi-modal human tactile demonstrations for contact-rich manipulation. In P. Agrawal, O. Kroemer, and W. Burgard, editors,Proceedings of The 8th Conference on Robot Learning, volume 270 of Procee...

    2025

  42. [50]

    Shukla, S

    R. Shukla, S. Moode, R. Talan, and S. K. Gupta. Learning Force-Conditioned Visuomotor Diffusion Policy from Human Demonstrations for Complex Robotic Assembly Tasks. InNorth American Manufacturing Research Conference (NAMRC) 2025, 2025

    2025

  43. [51]

    Mozaffari, D

    S. Mozaffari, D. Ruan, W. v. d. Bogert, N. Fazeli, S. Adriaenssens, and A. Adel. Learning dif- fusion policies for robotic manipulation of timber joinery under fabrication uncertainty.arXiv preprint arXiv:2511.17774, 2025

    2025 arXiv

  44. [52]

    Z. He, H. Fang, J. Chen, H.-S. Fang, and C. Lu. Foar: Force-aware reactive policy for contact- rich robotic manipulation.IEEE Robotics and Automation Letters, 10(6):5625–5632, 2025. doi:10.1109/LRA.2025.3560871

    2025

  45. [53]

    Haddadin and E

    S. Haddadin and E. Shahriari. Unified force-impedance control.International Journal of Robotics Research, 43(13):2112–2141, Nov. 2024. ISSN 0278-3649. doi:10.1177/ 02783649241249194. URLhttps://doi.org/10.1177/02783649241249194

    2024 doi

  46. [54]

    L. Wang, J. Bian, E. Heiden, and A. Garg. Topocut: Learning multi-step cutting with spectral rewards and discrete diffusion policies. In J. Lim, S. Song, and H.-W. Park, ed- itors,Proceedings of The 9th Conference on Robot Learning, volume 305 ofProceedings of Machine Learning...

  47. [55]

    Y . Han, J. Liu, L. Qi, and W. Xu. Digital twin modelling and its evaluation method of the robotic disassembly process.International Journal of Computer Integrated Manufacturing, pages 1–19, 2025. 25

    2025

  48. [56]

    Q. Qin, Z. Liu, R. Zhong, X. V . Wang, L. Wang, M. Wiktorsson, and W. Wang. Robot digital twin systems in manufacturing: Technologies, applications, trends and challenges.Robotics and Computer-Integrated Manufacturing, 97:103103, 2026

    2026

  49. [57]

    I. Díaz, D. Borro, O. Iparraguirre, M. Eizaguirre, F. A. Ricardo, N. Muñoz, and J. J. Gil. Robotic system for automated disassembly of electronic waste: Unscrewing.Robotics and Computer-Integrated Manufacturing, 95:103032, 2025. ISSN 0736-5845. doi:https:// doi.org/10.1016/j.r...

    2025

  50. [58]

    L. M. Ricard, E. Folkmann, L. C. Sørensen, S. B. Hybel, R. de Nóbrega, and H. G. Petersen. Design for robotic disassembly.Proceedings of the Design Society, 4:2715–2724, 2024

    2024

  51. [59]

    Simoni ˇc, A

    M. Simoni ˇc, A. Ude, and B. Nemec. Hierarchical learning of robotic contact policies.Robotics and computer-integrated manufacturing, 86:102657, 2024

    2024

  52. [60]

    Unger, C

    C. Unger, C. Hartl-Nesic, M. N. Vu, and A. Kugi. Prosip: Probabilistic surface interaction primitives for learning of robotic cleaning of edges. In2024 IEEE/RSJ International Confer- ence on Intelligent Robots and Systems (IROS), pages 5956–5963. IEEE, 2024

    2024

  53. [61]

    Y . Hu, C. Liu, M. Zhang, Y . Lu, Y . Jia, and Y . Xu. An ontology and rule-based method for human–robot collaborative disassembly planning in smart remanufacturing.Robotics and Computer-Integrated Manufacturing, 89:102766, 2024

    2024

  54. [62]

    M. H. Zafar, E. F. Langås, and F. Sanfilippo. Exploring the synergies between collaborative robotics, digital twins, augmentation, and industry 5.0 for smart manufacturing: A state-of- the-art review.Robotics and Computer-Integrated Manufacturing, 89:102769, 2024

    2024

  55. [63]

    El Kalach, M

    F. El Kalach, M. Farahani, T. Wuest, and R. Harik. Real-time defect detection and classification in robotic assembly lines: a machine learning framework.Robotics and Computer-Integrated Manufacturing, 95:103011, 2025

    2025

  56. [64]

    G. Lu, W. Guo, C. Zhang, Y . Zhou, H. Jiang, Z. Gao, Y . Tang, and Z. Wang. Vla-rl: To- wards masterful and general robotic manipulation with scalable reinforcement learning.arXiv preprint arXiv:2505.18719, 2025

    2025 arXiv

  57. [65]

    van den Oord, O

    A. van den Oord, O. Vinyals, and k. kavukcuoglu. Neural discrete representation learn- ing. In I. Guyon, U. V . Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors,Advances in Neural Information Processing Systems, volume 30. Curran Associates, ...

    2017

  58. [66]

    Rombach, A

    R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer. High-resolution image syn- thesis with latent diffusion models. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022. 26

    2022

  59. [71]

    URLhttp://www.roboticsproceedings.org/rss20/p071.pdf

  60. [2025]

    doi:10.48550/arXiv.2510.13324

    doi

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.