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REVIEW 3 major objections 6 minor 2 cited by

AsymDex: Asymmetry and Relative Coordinates for RL-based Bimanual Dexterity

T0 review · 3 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper argues that assigning complementary roles to two hands and controlling relative motion between them makes reinforcement learning efficient for bimanual dexterous manipulation, with zero-shot transfer to real hardware.

desk verdict AsymDex makes a solid empirical case for asymmetric roles and relative coordinates, but its claim that the reduced observation space loses no information is false in a gravity field. read the letter →

arxiv 2411.13020 v3 pith:AMGYBQZT submitted 2024-11-20 cs.RO

Zhaodong Yang , Yunhai Han , Ai-Ping Hu , Harish Ravichandar This is my paper · ORCID
classification cs.RO
keywords asymmetricbimanualmanipulationdexterousmulti-fingeredhandsreinforcementlearningrelativecoordinatessim-to-realtransfertwo-phaserole-differentiated
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 claims that two simple structural choices make reinforcement learning practical for bimanual tasks with multi-fingered hands, without needing demonstrations. First, instead of treating both hands symmetrically, assign one hand a fixed holding and reorienting role and the other the role of fine manipulation. Second, express the manipulator's pose relative to the object held by the holder, and split relative-pose commands between the two hands using a tracking controller. These choices shrink the observation and action spaces, and the paper reports strong gains in success rate and sample efficiency over baselines that remove either choice. If true, the framework extends demonstration-free RL to a broad class of everyday bimanual skills.

What carries the argument

The load-bearing mechanism is the combination of a role-asymmetric observation and action space with a relative-pose tracking controller. The policy observes the dominant hand's joint state plus the relative poses of the dominant hand base and the manipulated object with respect to the object held by the facilitating hand, and it outputs a target relative pose plus dominant-hand joint targets. The controller distributes the commanded relative pose between the two hand bases using a mixing coefficient, making synchronization a property of the coordinate frame rather than a behavior the policy must discover. This removes the facilitating hand's fingers from the learning problem and dramatically lowers dimensionality while coupling the hands through a shared relative target.

What would settle it

A direct falsifier: run AsymDex on a task where the holding hand must reorient the object inside its grasp, such as turning a bolt while the other hand threads a nut, and measure success against a version that retains the holding hand's finger joints; if the retained-joint version succeeds while AsymDex fails, the no-relative-motion premise is refuted.

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Extended reading notes

Core claim

AsymDex's central claim is that asymmetric bimanual dexterity—one hand holds and reorients an object while the other performs the fine manipulation—can be learned by reinforcement learning purely from reward, provided the policy operates in a reduced asymmetric space expressed in a frame attached to the held object. The paper defines a facilitating hand whose finger joints are omitted because there is no relative motion between it and the object it grasps, and a dominant hand whose full joint state and target pose are defined relative to the facilitating hand's object. A bimanual controller then converts the commanded relative pose into absolute base targets for both hands. In four simulated tasks (Block in cup, Stack, Bottle cap, Switch), AsymDex reaches success rates of 0.77, 0.84, 0.63, and 0.67, consistently beating baselines that drop either the asymmetry or the relative spaces. The same policies transfer zero-shot to physical hardware with 16/20, 17/20, and 18/20 successes on Block in cup, Pour, and Twist Lid, and the method can be combined with a learned grasping policy in a two-phase scheme that covers tabletop acquisition.

Load-bearing premise

The framework assumes the facilitating hand holds its object rigidly with no relative motion between that hand and the object, so the holding hand's finger joints can be dropped from the observation and action spaces; if a task requires in-hand reorientation by the holding hand, the reduced spaces cannot represent it.

Editorial extensions

If this is right

  • Demonstration-free RL becomes viable for a class of asymmetric bimanual tasks with multi-fingered hands, not just for a single handcrafted skill.
  • Observation and action dimensions drop substantially (from 176 to 88 and from 52 to 26 on the simulated tasks), which directly improves sample efficiency.
  • A two-phase decomposition lets the method combine learned grasping with the interaction policy, covering full tabletop initialization rather than only pre-grasped objects.
  • Sim-trained AsymDex policies transfer zero-shot to real hardware with high success rates on three physical bimanual tasks.

Reading between the lines

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

  • The paper leaves implicit that the same relative-frame idea could extend beyond two hands to multi-agent coordination, since the key step is expressing one agent's goal relative to an object another agent stabilizes.
  • A natural ablation for future work is keeping the dimensions matched while removing only the relative coordinates, which would isolate whether the gains come from the coordinate frame or just from reduced dimensionality.
  • The role assignment is prescribed rather than learned; a testable extension would let the policy discover which hand should serve as the facilitator for a given task.
  • The rigidity assumption suggests a hard boundary: tasks requiring in-hand reorientation by the holding hand would need a different representation, so the claimed class is best understood as tasks with no relative motion between the facilitating hand and its object.
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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 / 6 minor

Summary. The paper presents AsymDex, a reinforcement-learning framework for bimanual dexterous manipulation with multi-fingered hands. The method assigns asymmetric roles to the two hands—a facilitating hand that holds and reorients one object and a dominant hand that performs the fine manipulation—and defines the policy's observation and action spaces in the coordinate frame of the object held by the facilitating hand, together with a relative-pose tracking controller that converts relative target actions into absolute base motions. The paper also proposes a two-phase decomposition that separates object acquisition (grasping) from the coordinated interaction phase. AsymDex is evaluated in four simulated tasks (Block in cup, Stack, Bottle cap, Switch) and three real-world tasks (Block in cup, Pour, Twist Lid) against baselines that ablate the asymmetry and relative-space choices, and the authors report higher success rates and sample efficiency in simulation, plus zero-shot sim-to-real deployment on hardware.

Significance. If the results hold, AsymDex is a useful and simple framework for a practically important class of asymmetric bimanual manipulation tasks, and the simulation study is systematic: four tasks, five seeds, and three ablations that challenge each design choice. The paper also demonstrates zero-shot transfer for the proposed method on three real-world tasks. However, the theoretical justification for the observation-space reduction is incomplete, the key controller hyperparameter α is not reported, and some of the reported 'consistent' wins are within large error bars. The rigid-grasp scope is acknowledged in Sec. 7, but the gravity dependence of the relative representation is not. With these issues addressed, the paper would be a solid contribution.

major comments (3)
  1. [Sec. 4.2] The claim that ξ^b_f(t) and ξ^obj_f(t) can be removed from the observation 'without losing any information' is not valid in the presence of gravity. The observation z_AsymDex is invariant under a global rotation of the entire scene, but the dynamics and the rewards are not: the Pour reward R_cup_orient = z_cupf · z_world (Appendix D) explicitly depends on the absolute orientation of the facilitating hand's frame, and the Block-in-cup success criterion requires the cup to be oriented so that the released block falls into it. Two states that differ by a rotation of the whole scene about a horizontal axis produce identical observations but require different relative actions, because the direction of gravity in P_f differs. The controller in Eq. (1) provides the current frame orientation Rof_world, but it does not provide the direction of gravity in that frame, and the memoryless MLP policy with only the current observation and previous action cannot recover it. This is a genuine loss of information, not merely a restriction to tasks with rigid grasps; Sec. 7 lists other limitations but does not mention this gravity dependence. Please either add a gravity-direction vector expressed in P_f to the observation, or substantially qualify the losslessness claim and the 'large class' statement.
  2. [Sec. 4.3, Eq. (1)] The hyperparameter α is central to the bimanual controller, but its value is never reported and no ablation or sensitivity analysis is provided. The controller's behavior changes qualitatively with α: for α = 1 the facilitating hand base is stationary, while for α = 0.5 both hands share the relative motion. Without knowing α for each task, including whether it was tuned per task, the results in Tables 1–3 are not fully reproducible, and the comparison against the baselines may be affected by this choice. Please report α per task and include a sensitivity study.
  3. [Sec. 5.1.1, Table 1] The statement that AsymDex 'consistently outperforms' all baselines is only clearly supported for Block in cup and Switch. For Stack, Rel-w/o-Asym reaches 0.6560 ± 0.3213 versus 0.8392 ± 0.0596 for AsymDex, and for Bottle cap, Rel-w/o-Asym reaches 0.4730 ± 0.2011 versus 0.6295 ± 0.1422. With five seeds, these differences are within overlapping error bars and should not be described as consistent wins without a statistical test or per-seed reporting. Please add confidence intervals or p-values, or rephrase the conclusion to separate the tasks with large margins from those with small ones.
minor comments (6)
  1. [Algorithm 1] Lines 7 and 16 use the undefined symbols ξ^b_a(t) and ξ^h_r(t); presumably these should be ξ^b_d(t) and ξ^h_d(t).
  2. [Appendix C] The appendix title says 'BiDexHands' while the main text uses 'BiDexHand'; please unify the terminology.
  3. [Sec. 5.2] Because only AsymDex policies were deployed on hardware, the real-world section should explicitly state that it is a transfer demonstration, not a comparison, and the main text should say so as well.
  4. [Appendix B] The grasping policy for the 2-stage-sym baseline uses the relative observation ξ^b_f(t) ⊖ P_f, which appears to use a relative coordinate; please clarify whether this is shared infrastructure and not part of the baseline's 'no relative spaces' condition.
  5. [Eq. (1)] The pose difference dist(·) is not defined; please specify how translation and rotation are combined (e.g., quaternion log) and how the result is weighted by α.
  6. [Sec. 4.4 and Appendix B] The method uses pre-grasp pose annotations for both AsymDex and the baselines; the claim of learning 'without relying on demonstrations' should be qualified to clarify that task-specific pre-grasp knowledge is used.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: AsymDex's performance claims are empirical, benchmarked against baselines with task-defined success criteria.

full rationale

The paper's central claims are empirical: AsymDex is evaluated against ablation baselines (Sym, Asym-w/o-rel, Rel-w/o-Asym) on four simulated and three real-world tasks using success rates and sample-efficiency curves. The design choices — asymmetric hand roles and relative observation/action spaces — are architectural inductive biases, not quantities fitted to the reported outcomes. The observation-space reduction in Sec. 4.2 is justified by a stated physical assumption (no relative motion between the facilitating hand and its held object) and the removed quantities are not re-introduced as predictions. The relative-pose tracking controller in Eq. 1 is a deterministic design with a hyperparameter alpha, not a fitted parameter whose value determines the reported successes. No step reduces by construction: no defined quantity is defined in terms of the target result, no fitted input is renamed as a prediction, and no load-bearing premise depends on a self-citation. The skeptical concern about gravity breaking absolute-pose invariance is a potential correctness/sufficiency issue with the state representation, not an instance of circularity. Thus the finding is no significant circularity.

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

The central claim rests on a domain assumption (no in-hand movement of the facilitated object), a controller design with unstated alpha, and a set of task-specific reward hyperparameters.

free parameters (3)
  • alpha (relative involvement coefficient) = not reported in paper
    Eq. 1 uses alpha to split the relative-pose correction between dominant and facilitating hand bases; its value is a hand-chosen hyperparameter per task and is not stated.
  • Per-task reward weights = not reported
    Reward terms (Rhand distance, Rprogress, Raction penalty, Rsuccess bonus) are combined with weights that are task-specific and not listed in the paper.
  • Domain randomization ranges = see Table 6 (e.g., noise std dev 0.02-0.2, action noise 0.1, friction [0.5,1.5])
    Observation and action noise levels and friction ranges are chosen to enable sim-to-real transfer; values are not derived from data.
assumptions (3)
  • domain assumption In asymmetric bimanual tasks, the facilitating hand holds its object rigidly with no in-hand reorientation during interaction.
    Sec. 4.1: 'there tends to be no relative motion between the facilitating hand and the grasped object...' This justifies removing facilitating-hand finger states/actions; if false, the reduced spaces cannot represent needed behaviors.
  • ad hoc to paper The relative-pose controller in Eq. 1, with scalar alpha, can realize the coordinated base motions needed for task success.
    No stability or feasibility analysis is provided; the controller distributes all corrective motion between the two hands and its adequacy is only demonstrated empirically.
  • domain assumption PPO with a three-layer MLP policy and the provided reward signals can solve the reduced asymmetric MDP.
    The paper relies on standard RL training and reports empirical success, but provides no convergence guarantee; this is a common assumption in RL robotics.

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Cite this review

Pith. "Pith review of AsymDex: Asymmetry and Relative Coordinates for RL-based Bimanual Dexterity." pith.science (2026). https://pith.science/paper/AMGYBQZT

@misc{pith2026241113020,
  author       = {Pith},
  title        = {Pith review of: AsymDex: Asymmetry and Relative Coordinates for RL-based Bimanual Dexterity},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AMGYBQZT}},
  note         = {Machine review of arXiv:2411.13020}
}
read the original abstract

We present Asymmetric Dexterity (AsymDex), a novel and simple reinforcement learning (RL) framework that can efficiently learn a large class of bimanual skills in multi-fingered hands without relying on demonstrations. Two crucial insights enable AsymDex to reduce the observation and action space dimensions and improve sample efficiency. First, true ambidexterity is rare in humans and most of us exhibit strong "handedness". Inspired by this observation, we assign complementary roles to each hand: the facilitating hand repositions and reorients one object, while the dominant hand performs complex manipulations to achieve the desired result (e.g., opening a bottle cap, or pouring liquids). Second, controlling the relative motion between the hands is crucial for coordination and synchronization of the two hands. As such, we design relative observation and action spaces and leverage a relative-pose tracking controller. Further, we propose a two-phase decomposition in which AsymDex can be readily integrated with recent advances in grasp learning to facilitate both the acquisition and manipulation of objects using two hands. Unlike existing RL-based methods for bimanual dexterity with multi-fingered hands, which are either sample inefficient or tailored to a specific task, AsymDex can efficiently learn a wide variety of bimanual skills that exhibit asymmetry. Detailed experiments on seven asymmetric bimanual dexterous manipulation tasks (four simulated and three real-world) reveal that AsymDex consistently outperforms strong baselines that challenge our design choices. The project website is at https://sites.google.com/view/asymdex-2025/.

Figures

Figures reproduced from arXiv: 2411.13020 by the authors.

Figure 1
Figure 1. Our approach (AsymDex) efficiently learns asymmetric bimanual dexterous manipulation [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. AsymDex’s observation and Action Spaces. In addition to asymmetry, a key characteristic of bimanual dex￾terity is the synchronized and responsive movement of the two hands. We can further reduce the size of the observation and ac￾tion spaces and introduce tight coupling between the hands’ be￾haviors by defining relative and object-centric coordinates that capture the relationships between the movements of two hands … view at source ↗
Figure 3
Figure 3. We decompose asymmetric bimanual dexterous manipulation into two phases: An [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: AsymDex consistently outperforms the baselines in terms of sample efficiency and success [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: We created simulation environments to match our hardware setup. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Real-World Cooperative Bimanual Dexterous Grasp of Large Objects from Single-View Observations

    cs.RO 2026-08 conditional novelty 6.0 of 10

    A real-world pipeline trains a diffusion model on teleoperated bimanual demonstrations and adds force-guided refinement to grasp large objects from a single RGB-D view.

  2. You Only Teach Once: Learn One-Shot Bimanual Robotic Manipulation from Video Demonstrations

    cs.RO 2025-01 conditional novelty 6.0 of 10

    From one human hand demonstration, YOTO generates hundreds of robot demonstrations and trains a bimanual diffusion policy that outperforms ACT, DP, DP3, and EquiBot on five real-world tasks.

Reference graph

Works this paper leans on

55 extracted references · 35 canonical work pages · cited by 2 Pith papers

  1. [1]

    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

    work page 2012

  2. [2]

    Grannen, Y

    J. Grannen, Y . Wu, B. Vu, and D. Sadigh. Stabilize to act: Learning to coordinate for bimanual manipulation. In Conference on Robot Learning, pages 563–576. PMLR, 2023

    work page 2023

  3. [3]

    Avigal, L

    Y . Avigal, L. Berscheid, T. Asfour, T. Kr¨oger, and K. Goldberg. Speedfolding: Learning effi- cient bimanual folding of garments. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 1–8. IEEE, 2022

    work page 2022

  4. [4]

    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. arXiv preprint arXiv:2402.10329, 2024

    arXiv 2024

  5. [5]

    C. Wang, H. Shi, W. Wang, R. Zhang, L. Fei-Fei, and C. K. Liu. Dexcap: Scalable and portable mocap data collection system for dexterous manipulation. arXiv preprint arXiv:2403.07788, 2024

    arXiv 2024

  6. [6]

    Zakka, P

    K. Zakka, P. Wu, L. Smith, N. Gileadi, T. Howell, X. B. Peng, S. Singh, Y . Tassa, P. Florence, A. Zeng, et al. Robopianist: Dexterous piano playing with deep reinforcement learning. arXiv preprint arXiv:2304.04150, 2023

    arXiv 2023

  7. [7]

    Rajeswaran, V

    A. Rajeswaran, V . Kumar, A. Gupta, G. Vezzani, J. Schulman, E. Todorov, and S. Levine. Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demon- strations. In Proceedings of Robotics: Science and Systems (RSS), 2018

    work page 2018

  8. [8]

    Andrychowicz, B

    OpenAI, M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray, J. Schneider, S. Sidor, J. Tobin, P. Welinder, L. Weng, and W. Zaremba. Learning Dexterous In-Hand Manipulation. International Journal of Robotics Research (IJRR), 2020

    work page 2020

Show all 55 references
  1. [9]

    H. Qi, A. Kumar, R. Calandra, Y . Ma, and J. Malik. In-hand object rotation via rapid motor adaptation. arXiv preprint arXiv:2210.04887, 2022

    2022 arXiv

  2. [10]

    Y . Han, M. Xie, Y . Zhao, and H. Ravichandar. On the utility of koopman operator theory in learning dexterous manipulation skills. In Conference on Robot Learning, pages 106–126. PMLR, 2023

    2023

  3. [11]

    Y . Han, Z. Chen, K. A. Williams, and H. Ravichandar. Learning prehensile dexterity by im- itating and emulating state-only observations. IEEE Robotics and Automation Letters, 9(10): 8266–8273, 2024

    2024

  4. [12]

    H. Chen, A. ABUDUWEILI, A. Agrawal, Y . Han, H. Ravichandar, C. Liu, and J. Ichnowski. Korol: Learning visualizable object feature with koopman operator rollout for manipulation. In 8th Annual Conference on Robot Learning

  5. [13]

    K. Shaw, S. Bahl, A. Sivakumar, A. Kannan, and D. Pathak. Learning dexterity from hu- man hand motion in internet videos. The International Journal of Robotics Research , page 02783649241227559, 2024

    2024

  6. [14]

    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

    2022

  7. [15]

    Y . Chen, C. Wang, Y . Yang, and C. K. Liu. Object-centric dexterous manipulation from human motion data. arXiv preprint arXiv:2411.04005, 2024. 10

    2024 arXiv

  8. [16]

    R.-Z. Qiu, S. Yang, X. Cheng, C. Chawla, J. Li, T. He, G. Yan, L. Paulsen, G. Yang, S. Yi, et al. Humanoid policy˜ human policy. arXiv preprint arXiv:2503.13441, 2025

    2025

  9. [17]

    Jiang, Y

    Z. Jiang, Y . Xie, K. Lin, Z. Xu, W. Wan, A. Mandlekar, L. Fan, and Y . Zhu. Dexmimicgen: Automated data generation for bimanual dexterous manipulation via imitation learning. arXiv preprint arXiv:2410.24185, 2024

    2024 arXiv

  10. [18]

    B. Zhou, H. Yuan, Y . Fu, and Z. Lu. Learning diverse bimanual dexterous manipulation skills from human demonstrations. arXiv preprint arXiv:2410.02477, 2024

    2024 arXiv

  11. [19]

    Lin, Z.-H

    T. Lin, Z.-H. Yin, H. Qi, P. Abbeel, and J. Malik. Twisting lids off with two hands. arXiv preprint arXiv:2403.02338, 2024

    2024 arXiv

  12. [20]

    Huang, Y

    B. Huang, Y . Chen, T. Wang, Y . Qin, Y . Yang, N. Atanasov, and X. Wang. Dynamic handover: Throw and catch with bimanual hands. arXiv preprint arXiv:2309.05655, 2023

    2023 arXiv

  13. [21]

    Y . Guiard. Asymmetric division of labor in human skilled bimanual action: The kinematic chain as a model. Journal of motor behavior, 19(4):486–517, 1987

    1987

  14. [22]

    Kimmerle, C

    M. Kimmerle, C. L. Ferre, K. A. Kotwica, and G. F. Michel. Development of role-differentiated bimanual manipulation during the infant’s first year.Developmental Psychobiology: The Jour- nal of the International Society for Developmental Psychobiology, 52(2):168–180, 2010

    2010

  15. [23]

    R. L. Sainburg. Evidence for a dynamic-dominance hypothesis of handedness. Experimental brain research, 142:241–258, 2002

    2002

  16. [24]

    B. E. Studenka and H. N. Zelaznik. The influence of dominant versus non-dominant hand on event and emergent motor timing. Human Movement Science, 27(1):29–52, 2008

    2008

  17. [25]

    Cashmore, N

    L. Cashmore, N. Uomini, and A. Chapelain. The evolution of handedness in humans and great apes: a review and current issues. Journal of anthropological sciences, 86(2008):7–35, 2008

    2008

  18. [26]

    Bahety, P

    A. Bahety, P. Mandikal, B. Abbatematteo, and R. Mart ´ın-Mart´ın. Screwmimic: Bimanual imitation from human videos with screw space projection. arXiv preprint arXiv:2405.03666, 2024

    2024 arXiv

  19. [27]

    Franzese, L

    G. Franzese, L. de Souza Rosa, T. Verburg, L. Peternel, and J. Kober. Interactive imitation learning of bimanual movement primitives. IEEE/ASME Transactions on Mechatronics, pages 1–13, 2023

    2023

  20. [28]

    M. Seo, S. Han, K. Sim, S. H. Bang, C. Gonzalez, L. Sentis, and Y . Zhu. Deep imitation learning for humanoid loco-manipulation through human teleoperation. In 2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids), pages 1–8. IEEE, 2023

    2023

  21. [29]

    T. Z. Zhao, V . Kumar, S. Levine, and C. Finn. Learning fine-grained bimanual manipulation with low-cost hardware. arXiv preprint arXiv:2304.13705, 2023

    2023 arXiv

  22. [30]

    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

  23. [31]

    Kataoka, S

    S. Kataoka, S. K. S. Ghasemipour, D. Freeman, and I. Mordatch. Bi-manual manipulation and attachment via sim-to-real reinforcement learning. arXiv preprint arXiv:2203.08277, 2022

    2022 arXiv

  24. [32]

    Chitnis, S

    R. Chitnis, S. Tulsiani, S. Gupta, and A. Gupta. Efficient bimanual manipulation using learned task schemas. In 2020 IEEE International Conference on Robotics and Automation (ICRA) , pages 1149–1155. IEEE, 2020

    2020

  25. [33]

    Y . Li, C. Pan, H. Xu, X. Wang, and Y . Wu. Efficient bimanual handover and rearrangement via symmetry-aware actor-critic learning. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 3867–3874. IEEE, 2023. 11

    2023

  26. [34]

    H. Jo, W. Choi, G. Lee, W. Park, and J. Kim. Analysis of visuo motor control between dominant hand and non-dominant hand for effective human-robot collaboration. Sensors, 20(21):6368, 2020

    2020

  27. [35]

    Holladay, T

    R. Holladay, T. Lozano-P ´erez, and A. Rodriguez. Robust planning for multi-stage forceful manipulation. The International Journal of Robotics Research, 43(3):330–353, 2024

    2024

  28. [36]

    Grannen, Y

    J. Grannen, Y . Wu, S. Belkhale, and D. Sadigh. Learning bimanual scooping policies for food acquisition. arXiv preprint arXiv:2211.14652, 2022

    2022 arXiv

  29. [37]

    J. Liu, Y . Chen, Z. Dong, S. Wang, S. Calinon, M. Li, and F. Chen. Robot cooking with stir-fry: Bimanual non-prehensile manipulation of semi-fluid objects. IEEE Robotics and Automation Letters, 7(2):5159–5166, 2022

    2022

  30. [38]

    Y . Cui, Z. Xu, L. Zhong, P. Xu, Y . Shen, and Q. Tang. A task-adaptive deep reinforcement learning framework for dual-arm robot manipulation. IEEE Transactions on Automation Sci- ence and Engineering, 2024

    2024

  31. [39]

    Nagabandi, K

    A. Nagabandi, K. Konolige, S. Levine, and V . Kumar. Deep dynamics models for learning dexterous manipulation. In Conference on Robot Learning, pages 1101–1112. PMLR, 2020

    2020

  32. [40]

    Qin, Y .-H

    Y . Qin, Y .-H. Wu, S. Liu, H. Jiang, R. Yang, Y . Fu, and X. Wang. Dexmv: Imitation learning for dexterous manipulation from human videos. In European Conference on Computer Vision, pages 570–587. Springer, 2022

    2022

  33. [41]

    Khandate, S

    G. Khandate, S. Shang, E. T. Chang, T. L. Saidi, J. Adams, and M. Ciocarlie. Sampling- based Exploration for Reinforcement Learning of Dexterous Manipulation. In Proceedings of Robotics: Science and Systems , Daegu, Republic of Korea, July 2023. doi:10.15607/RSS. 2023.XIX.020

    2023 doi

  34. [42]

    M. Xie, A. Handa, S. Tyree, D. Fox, H. Ravichandar, N. D. Ratliff, and K. Van Wyk. Neu- ral geometric fabrics: Efficiently learning high-dimensional policies from demonstration. In Conference on Robot Learning, pages 1355–1367. PMLR, 2023

    2023

  35. [43]

    Handa, K

    A. Handa, K. Van Wyk, W. Yang, J. Liang, Y .-W. Chao, Q. Wan, S. Birchfield, N. Ratliff, and D. Fox. Dexpilot: Vision-based teleoperation of dexterous robotic hand-arm system. In 2020 IEEE International Conference on Robotics and Automation (ICRA), pages 9164–9170. IEEE, 2020

    2020

  36. [44]

    S. P. Arunachalam, I. G ¨uzey, S. Chintala, and L. Pinto. Holo-dex: Teaching dexterity with immersive mixed reality. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 5962–5969. IEEE, 2023

    2023

  37. [45]

    T. Chen, E. Cousineau, N. Kuppuswamy, and P. Agrawal. Vegetable peeling: A case study in constrained dexterous manipulation. In Towards Generalist Robots: Learning Paradigms for Scalable Skill Acquisition@ CoRL2023, 2023

    2023

  38. [46]

    R. Laha, J. V orndamme, L. F. Figueredo, Z. Qu, A. Swikir, C. J ¨ahne, and S. Haddadin. Co- ordinated motion generation and object placement: A reactive planning and landing approach. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pages 94...

    2021

  39. [47]

    Chiacchio, S

    P. Chiacchio, S. Chiaverini, and B. Siciliano. Direct and inverse kinematics for coordinated motion tasks of a two-manipulator system. 1996

    1996

  40. [48]

    Tarbouriech, B

    S. Tarbouriech, B. Navarro, P. Fraisse, A. Crosnier, A. Cherubini, and D. Sall ´e. Dual-arm relative tasks performance using sparse kinematic control. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 6003–6009. IEEE, 2018. 12

    2018

  41. [49]

    Schulman, F

    J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov. Proximal policy optimization algorithms, 2017

    2017

  42. [50]

    Dasari, A

    S. Dasari, A. Gupta, and V . Kumar. Learning dexterous manipulation from exemplar object tra- jectories and pre-grasps. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 3889–3896. IEEE, 2023

    2023

  43. [51]

    Liang, V

    J. Liang, V . Makoviychuk, A. Handa, N. Chentanez, M. Macklin, and D. Fox. Gpu-accelerated robotic simulation for distributed reinforcement learning, 2018

    2018

  44. [52]

    A. . Team, J. Aldaco, T. Armstrong, R. Baruch, J. Bingham, S. Chan, K. Draper, D. Dwibedi, C. Finn, P. Florence, S. Goodrich, W. Gramlich, T. Hage, A. Herzog, J. Hoech, T. Nguyen, I. Storz, B. Tabanpour, L. Takayama, J. Tompson, A. Wahid, T. Wahrburg, S. Xu, S. Yaroshenko, K. ...

    2024 arXiv

  45. [53]

    Cheng, J

    X. Cheng, J. Li, S. Yang, G. Yang, and X. Wang. Open-television: Teleoperation with im- mersive active visual feedback. In 8th Annual Conference on Robot Learning , 2024. URL https://openreview.net/forum?id=Yce2jeILGt

    2024

  46. [54]

    R. Ding, Y . Qin, J. Zhu, C. Jia, S. Yang, R. Yang, X. Qi, and X. Wang. Bunny-visionpro: Real- time bimanual dexterous teleoperation for imitation learning, 2024. URL https://arxiv. org/abs/2407.03162

    2024 arXiv

  47. [55]

    K. Shaw, Y . Li, J. Yang, M. K. Srirama, R. Liu, H. Xiong, R. Mendonca, and D. Pathak. Bimanual dexterity for complex tasks. In 8th Annual Conference on Robot Learning , 2024. URL https://openreview.net/forum?id=55tYfHvanf. 13 Appendices A Training Details We use Proximal Poli...

    2024

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.