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REVIEW 3 major objections 5 minor 33 references

Tri-Manual Visuomotor Imitation Learning of Robot Policies

T0 review · 3 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read A dependency-aware retiming step turns pairwise teleoperation demonstrations into synchronous three-arm training data, making learned robot policies 42% faster with equal success.

desk verdict Novel retiming-as-scheduling idea with an honest matched real-robot evaluation; the weak spot is the untested physical coherence of retimed composites, and that deserves a direct audit before the result is treated as robust. read the letter →

arxiv 2607.25731 v3 pith:RZ432M3U submitted 2026-07-28 cs.RO

James Zhao , Mingyuan Ba , Weiming Zhi This is my paper
classification cs.RO
keywords imitationlearningmulti-armmanipulationteleoperationtemporalretimingconstraintschedulingbehaviorcloningvisuomotorpolicydependencygraph
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 is trying to establish that a robot with more arms than a single human operator can control at once can still be taught by imitation from that operator, provided the training data are retimed to remove interface-imposed delays. The proposed DATS method keeps each demonstrated arm motion intact but repositions segments on a new timeline that respects task dependencies and arm availability, then trains one synchronous policy for all three arms. On six real-world tasks, policies trained on retimed demonstrations completed successful trials 42.5% faster on average while matching the baseline's success count. The authors argue this is a genuine change in what the policy is supervised to do — compatible arm behaviors appear together in the same 2-second action windows — not merely removal of idle time.

What carries the argument

The central object is Dependency-Aware Tri-Arm Scheduling (DATS), a constrained scheduling formulation. Each demonstrated episode is segmented into fixed-duration subtask intervals annotated with required-arm sets and predecessor relations, reviewed by a human to encode task prerequisites and shared-workspace orderings. DATS solves a makespan-minimization optimization problem — using a constraint-optimization solver — that enforces finish-to-start precedence edges and per-arm no-overlap constraints, producing a new start time for every segment. The mapping preserves local sensorimotor timing within segments while composing arm streams recorded at different raw times into new observation-acti

What would settle it

Take one task (e.g., tote-card insertion) and corrupt the dependency graph by randomly permuting predecessor edges while keeping the same segment motions; train policies on DATS with the shuffled graphs. If success remains at the reported level and time reduction persists, the graph annotation is not the source of the gain; if success collapses, the human-reviewed dependencies are doing the work. A second check: use motion capture during overlaps to measure whether the retimed composite states actually occur without contact violations.

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Extended reading notes

Core claim

The central claim is that a mode-switched teleoperation demonstration contains the right local motions on the wrong global clock: an arm waits because the operator is controlling another pair, not because the task requires a delay. DATS replaces that clock with one defined by a human-reviewed dependency graph and arm-resource constraints, solving a fixed-duration interval scheduling problem that minimizes total episode duration. The resulting retimed streams are what train a synchronous action-chunked transformer policy for all three arms. Across 237 demonstrations and 300 real-robot trials on six tasks, DATS-trained policies achieved 129/150 successes versus 126/150 for baseline, with avera

Load-bearing premise

The load-bearing premise is that arm-centric motion segments recorded at different raw times remain locally meaningful when composed into new observation-action windows — if the retimed composites are not physically coherent sensorimotor context, the policy could learn from impossible states and the real-robot success would be a lucky artifact; this depends on the human-reviewed graph correctly encoding every task prerequisite and on interface-induced delays being separable f

Editorial extensions

If this is right

  • Deployment needs no dependency graph or scheduler: the retimed data train a policy that acts directly from observations, so the added machinery lives entirely in offline data construction.
  • One operator can demonstrate tasks for three (or, by the same resource formulation, more) arms through pairwise control rather than requiring multi-user teleoperation.
  • Retiming yields faster coordinated execution on all six tasks — time reductions between 31% and 50% — without sacrificing observed success.
  • The same exclusivity constraint can encode non-arm resources such as tools or workspace regions, so the method generalizes beyond manipulators.
  • For two tasks the timeline hardly shortened, yet co-window coverage rose sharply, showing that retiming can improve joint supervision even when episode duration is unchanged.

Reading between the lines

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

  • The central assumption that graph-compatible segments remain physically coherent once composed could be tested directly by running DATS-retimed policies with dependency graphs whose precedence edges are randomly shuffled; if success survives, the human-reviewed graph is not doing the load-bearing work.
  • The method suggests a general principle for imitation learning: separate 'what to do' from 'when to do it' when the collection interface is less parallel than the embodiment, which may apply to asymmetric bimanual setups, mobile manipulators, or any channel-limited teaching interface.
  • Because DATS only relocates recorded segments, its ceiling is set by the coverage of the original demonstrations; tasks requiring genuinely novel cross-arm coordination outside the raw data would need additional collection, not just retiming.
  • A natural next step is to automate the dependency-graph annotation (currently proposed by a vision-language model and reviewed by a human); if that automation matures, DATS becomes a drop-in data preprocessing module for existing action-chunked imitation pipelines.
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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. TriManPolicy extends teleoperated imitation learning to a three-arm robot using a single operator who switches between two-arm control modes. The central component, Dependency-Aware Tri-Arm Scheduling (DATS), takes human-reviewed subtask graphs, preserves fixed-duration arm-centric segments, and reschedules them under predecessor and per-arm non-overlap constraints by minimizing episode makespan. Retimed demonstrations train a single action-chunked transformer policy that controls all three arms synchronously. In six real-world tasks with 237 processed demonstrations and 25 trials per condition, DATS-trained policies complete successful trials faster (macro-mean 47.8 s vs. 83.1 s) with comparable observed success (129/150 vs. 126/150). Offline diagnostics separate duration reduction from changes in which cross-arm segment pairs share training windows.

Significance. If the central claim holds, the paper makes a useful contribution: it identifies interface-induced timing as a learnable artifact in imitation learning and proposes a simple, interpretable scheduling transformation that preserves demonstrated local motions while changing their global placement. The evaluation is genuinely matched—same demonstrations, same policy architecture, same trial protocol, interleaved trials—and the six-task suite gives the result breadth. The offline decomposition of duration change from target-window composition is a thoughtful diagnostic, and the use of CP-SAT with all 237 graphs solved is a practical strength. The main uncertainty is whether the retimed composite observations are physically coherent, since the closed-loop success counts are only an indirect test of that assumption.

major comments (3)
  1. [Sec. III-E, Eq. (9)] The load-bearing assumption that 'graph-compatible arm-centric segments remain locally meaningful when composed' is stated but not directly validated. Eq. (9) shifts each arm's samples by a segment offset, so the constructed observation windows combine arm-centric streams recorded at different raw timestamps. The scheduler enforces NoOverlap per arm but, as noted in Sec. III-D, it does not impose raw order on same-arm segments; a same-arm pair with no dependency path can in principle be reordered, changing arm-local continuity at segment boundaries. Aggregate real-robot success (Sec. IV-B) is a weak isolation: a policy can ignore inconsistent streams in some windows and still succeed on 25 trials. Please add a direct fused-state audit, e.g., verify that composed observations satisfy the checked object-state predicates at segment boundaries, or simulate the retimed arm trajectories to che
  2. [Sec. IV-B, Table II] The headline empirical claims rest on 25 trials per condition and a single training seed. The aggregate success difference is 129/150 vs. 126/150, and per-task differences are a few successes. No confidence intervals or significance tests are reported, and completion times are computed only on successful trials, which introduces a selection effect. Please report bootstrap confidence intervals for per-task and aggregate success and time, use appropriate per-task tests (e.g., Fisher exact for success counts, paired bootstrap for time), and ideally retrain with multiple seeds. The Discussion's sentence in Sec. IV-E correctly acknowledges that one seed does not estimate variation across training seeds, but this limitation should appear in the main results, not only as a caveat.
  3. [Sec. IV-C, Tables III–IV, Eq. (11)] The co-window coverage audit measures temporal co-occurrence inside a fixed 2.0 s horizon, so it is a direct readout of the interval-scheduling objective: it counts how many eligible pairs are placed in a common window. It cannot validate that the newly composed streams are physically coherent, and the paper itself treats Gap as an offline diagnostic rather than a trained condition. As written, the offline analysis supports the claim that DATS changes which segment targets share a window, but not the stronger claim that the resulting supervision is executable. Please state this distinction explicitly in the main text. If the decomposition is intended to support a mechanistic explanation of the rollout results, training a policy on Gap timelines would be needed to separate duration effects from target-composition effects.
minor comments (5)
  1. [Sec. III-D, Eq. (5)] The notation is inconsistent: the raw interval uses hats, but the scheduled start and end are then written as s_j and e_j without hats, and the equation appears as 'ej = s_j + d_j'. Please unify the notation, e.g., use s_j^*, e_j^* for scheduled times throughout.
  2. [Fig. 6] The caption states that all 237 episodes show higher DATS than Gap co-window coverage, but no paired effect size or confidence interval is given. Add a paired summary (e.g., mean paired difference with CI) to quantify the consistency.
  3. [Sec. IV-C, Eq. (11)] It is clear that each eligible pair contributes at most once, but the denominator |C_n| counts pairs, while the numerator counts pairs with at least one valid window. Consider stating explicitly that a pair is counted once even if it co-occurs in multiple windows.
  4. [Sec. IV-B] Table II reports mean completion time over successful trials only, but does not report the number of successful trials used for the time mean per task (the Succ. column gives this, so this is a minor formatting point). Please clarify in the table caption that the time statistics are conditional on success.
  5. [Sec. IV-D] The selected rollouts in Figs. 7–9 are illustrative and necessarily cherry-picked. This is acceptable, but the caption should explicitly say that these are selected successful rollouts and not representative of all trials.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: DATS is an explicit data transformation with real-robot evaluation; self-citations are not load-bearing.

full rationale

The paper's claimed derivation chain does not reduce to its inputs. DATS is a clearly specified data transformation: it takes annotated, fixed-duration segments and optimizes their start times under predecessor and per-arm non-overlap constraints (Eqs. 5–8). The reported speed-up is an empirical robot outcome, not an equation-level consequence: a behavior-cloning policy trained on shorter supervision could fail to transfer if the retimed composite observations were physically incoherent, and the real-robot trials (Table II) provide an external, falsifiable test. The co-window coverage diagnostic (Eq. 11) is presented as a descriptive audit of the constructed supervision, explicitly contrasted with Gap and with deployment time, and the paper states it measures target-window composition, not concurrency quality. The central assumption in Sec. III-E — that graph-compatible arm-centric segments remain locally meaningful when composed — is explicitly stated as an assumption and then tested by closed-loop robot trials; it is not silently folded into a predicted result. Self-citations [5, 11–14, 28] appear only in related-work comparisons and are not used to justify the method, invoke a uniqueness theorem, or forbid alternatives. No fitted parameter is renamed as a prediction, and no ansatz is smuggled in via self-citation. Consequently, no specific circular step can be exhibited; the contribution is empirically grounded, with the flagged composability assumption being a limitation rather than a circularity.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The central claim rests on two domain assumptions the paper explicitly names: the reviewed graph is a correct task specification, and retimed segments remain locally meaningful. The free parameters are standard hyperparameters, not fitted to the outcome. No new physical entities are postulated.

free parameters (2)
  • Action horizon H = 100 steps (2.0 s)
    Hand-chosen chunking horizon; co-window coverage and target-window composition depend on it, so it is a design choice rather than an empirical constant.
  • Tick duration Delta t = 0.01 s
    Discretization step for scheduling; chosen small relative to motion timescales and not fitted to outcome data.
assumptions (5)
  • domain assumption Human-reviewed subtask graph G_n correctly encodes task prerequisites and required-arm sets.
    DATS guarantees precedence and no-overlap relative to the annotated graph; if the graph misses a dependency, retiming can create infeasible or unsafe supervision. Stated in Sec. III-C: 'For DATS, this reviewed graph is the episode-level task specification.'
  • domain assumption Graph-compatible arm-centric segments remain locally meaningful when composed after retiming.
    Explicitly stated in Sec. III-E: 'This construction assumes that graph-compatible arm-centric segments remain locally meaningful when composed.' This is load-bearing because DATS stitches segments from different raw times into new observation-action windows.
  • domain assumption Interface-induced delays can be separated from task-required delays using the dependency graph.
    The motivating premise in Sec. I: demonstrated motions remain useful even when recorded timing does not. If mode-switching also distorts kinematics or object interaction dynamics, retiming could break task semantics.
  • standard math CP-SAT/OR-Tools solves the formulated interval scheduling problem correctly and optimally.
    The solver is treated as a black-box oracle; the paper reports all 237 instances solved to optimality under the 30 s limit (Sec. IV-A), but no formal proof or certificate is provided.
  • domain assumption Observation space (arm-centric RGB + proprioception) is sufficient for closed-loop control under retimed supervision.
    The action-chunked transformer is trained on these observations; if retiming produces physically inconsistent observations, closed-loop execution could fail. The real-robot trials test this indirectly but do not isolate it.

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Pith. "Pith review of Tri-Manual Visuomotor Imitation Learning of Robot Policies." pith.science (2026). https://pith.science/paper/RZ432M3U

@misc{pith2026260725731,
  author       = {Pith},
  title        = {Pith review of: Tri-Manual Visuomotor Imitation Learning of Robot Policies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RZ432M3U}},
  note         = {Machine review of arXiv:2607.25731}
}
read the original abstract

Bimanual teleoperation provides an effective way to collect robot demonstrations, but it assumes that the operator and robot have matching numbers of simultaneous control channels. This assumption breaks for tri-manual systems: the robot can coordinate three arms concurrently, whereas a single operator can continuously control only two. Pairwise mode switching may therefore record otherwise independent motions sequentially, causing behaviour cloning to reproduce delays imposed by the interface rather than required by the task. We present TriManPolicy, a tri-manual imitation learning system that allows one operator to demonstrate behaviours for three arms. Its central component is Dependency-Aware Tri-Arm Scheduling (DATS). The key idea is to preserve the demonstrated arm motions while reconsidering when they occur. DATS retimes demonstrations offline by preserving local sensorimotor segments of fixed duration and repositioning them according to constraints on task order and arm usage that are reviewed by a human. The resulting data train a single synchronous policy for all three arms, while deployment requires neither the dependency graph nor the scheduler. Across six challenging tasks performed in the real world, policies trained on demonstrations retimed by DATS exhibit more efficient coordination while maintaining comparable observed task success. Offline analysis further shows that DATS changes the supervision across arms rather than merely removing idle periods. Project videos and additional material are available at https://aus.bot/research/trimanpolicy/.

Figures

Figures reproduced from arXiv: 2607.25731 by the authors.

Figure 1
Figure 1. TriManPolicy learns synchronous three-arm visuomotor policies from pairwise demonstrations collected by one operator. Pairwise control can serialise independent actions; DATS reconstructs their global timing before policy training. Selected rollouts show the auxiliary arm maintaining task geometry while the other arms manipulate. Abstract— Bimanual teleoperation provides an effective way to collect robot demonstrati… view at source ↗
Figure 2
Figure 2. TriManPolicy pipeline. An offline annotation pass constructs a reviewed subtask graph from each mode-switched demonstration; DATS uses that graph to optimise segment placement and produce retimed three-arm supervision. Raw and DATS share the policy-learning module. sketches, or cross-modal annotations [11]–[14]. Bimanual systems support stabilisation and handover [15]. MART learns two- and three-arm coordination fro… view at source ↗
Figure 3
Figure 3. Mode-switched teleoperation interface for three-arm demonstration collection. The interface supports pairwise control assignments over Arms L, R, and O, allowing one operator to collect three-arm demonstrations while controlling only two arms at a time. episode-level task specification. The author checks object￾state prerequisites and intervals that establish or maintain support; automated validation covers schema v… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: DATS temporal reparameterisation on BAGTAPE. Equal-width purple bands schematically mark horizon-H action-target windows. The Raw window contains bag preparation on O with held targets on L and R; the DATS window presents tape handling on R together with bag preparatio…
Figure 5
Figure 5. Figure 5: Selected successful DATS rollouts for BINTOWEL (top) and TOWELHANG (bottom), showing O progressing while L and R continue towel manipulation. Baseline DATS Task N Succ. Time (s) Succ. Time (s) Time red. (%) TOWELHANG 47 17/25 150.4±4.7 19/25 83.6±0.8 44.4 BINTOWEL 30 2…
Figure 7
Figure 7. Figure 7: Selected TOWELHANG rollout: hanger retrieval overlaps towel folding and lifting [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Selected TRAYWIPE rollout: the DATS-trained policy begins towel pickup during tray stabilisation [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]
Figure 9
Figure 9. Figure 9: Selected LIDERASER rollout: the DATS-trained policy has acquired both erasers while the baseline is still waiting. towel preparation, towel pickup begins during sustained tray support, and both erasers are acquired during lid positioning. Across these examples, require…

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Works this paper leans on

33 extracted references · 2 linked inside Pith

  1. [1]

    Learning fine-grained bimanual manipulation with low-cost hardware,

    T. Z. Zhao, V . Kumar, S. Levine, and C. Finn, “Learning fine-grained bimanual manipulation with low-cost hardware,” inRobotics: Science and Systems, 2023

    2023

  2. [2]

    Mobile ALOHA: Learning bimanual mobile manipulation using low-cost whole-body teleoperation,

    Z. Fu, T. Z. Zhao, and C. Finn, “Mobile ALOHA: Learning bimanual mobile manipulation using low-cost whole-body teleoperation,” in Proceedings of the 8th Conference on Robot Learning, ser. Proceed- ings of Machine Learning Research, vol. 270. PMLR, 2025, pp. 4066–4083

    2025

  3. [3]

    A survey of robot learning from demonstration,

    B. D. Argall, S. Chernova, M. Veloso, and B. Browning, “A survey of robot learning from demonstration,”Robotics and Autonomous Systems, vol. 57, no. 5, pp. 469–483, 2009

    2009

  4. [4]

    Recent advances in robot learning from demonstration,

    H. Ravichandar, A. S. Polydoros, S. Chernova, and A. Billard, “Recent advances in robot learning from demonstration,”Annual Review of Control, Robotics, and Autonomous Systems, vol. 3, pp. 297–330, 2020

    2020

  5. [5]

    Diffeomorphic transforms for generalised imitation learning,

    W. Zhi, T. Lai, L. Ott, and F. Ramos, “Diffeomorphic transforms for generalised imitation learning,” inProceedings of the 4th Annual Learning for Dynamics and Control Conference, ser. Proceedings of Machine Learning Research, vol. 168. PMLR, 2022, pp. 508–519

    2022

  6. [6]

    Diffusion policy: Visuomotor policy learning via action diffusion,

    C. Chi, S. Feng, Y . Du, Z. Xu, E. Cousineau, B. C. M. Burchfiel, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” inProceedings of Robotics: Science and Systems, 2023

    2023

  7. [7]

    ALOHA unleashed: A simple recipe for robot dexterity,

    T. Z. Zhao, J. Tompson, D. Driess, P. Florence, K. Ghasemipour, C. Finn, and A. Wahid, “ALOHA unleashed: A simple recipe for robot dexterity,” inProceedings of The 8th Conference on Robot Learning, ser. Proceedings of Machine Learning Research, vol. 270. PMLR, 2025, pp. 1910–1924

    2025

  8. [8]

    Universal manipulation interface: In-the-wild robot teaching without in-the-wild robots,

    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,” inProceedings of Robotics: Science and Systems, 2024

    2024

Show all 33 references
  1. [9]

    DROID: A large-scale in-the-wild robot manipulation dataset,

    A. Khazatsky, K. Pertsch, S. Nair, A. Balakrishna, S. Dasari, S. Karam- cheti, S. Nasiriany, M. K. Srirama,et al., “DROID: A large-scale in-the-wild robot manipulation dataset,” inProceedings of Robotics: Science and Systems, 2024

    2024

  2. [10]

    MimicGen: A data generation system for scalable robot learning using human demonstrations,

    A. Mandlekar, S. Nasiriany, B. Wen, I. Akinola, Y . Narang, L. Fan, Y . Zhu, and D. Fox, “MimicGen: A data generation system for scalable robot learning using human demonstrations,” inProceedings of The 7th Conference on Robot Learning, ser. Proceedings of Machine Learning Res...

    2023

  3. [11]

    Joint flow trajectory optimization for feasible robot motion generation from video demon- strations,

    X. Dong, M. Johnson-Roberson, and W. Zhi, “Joint flow trajectory optimization for feasible robot motion generation from video demon- strations,” in2026 IEEE International Conference on Robotics and Automation, 2026

    2026

  4. [12]

    Instructing robots by sketching: Learning from demonstration via probabilistic diagram- matic teaching,

    W. Zhi, T. Zhang, and M. Johnson-Roberson, “Instructing robots by sketching: Learning from demonstration via probabilistic diagram- matic teaching,” in2024 IEEE International Conference on Robotics and Automation, 2024, pp. 15 047–15 053

    2024

  5. [13]

    Teaching periodic stable robot motion generation via sketch,

    W. Zhi, H. Tang, T. Zhang, and M. Johnson-Roberson, “Teaching periodic stable robot motion generation via sketch,”IEEE Robotics and Automation Letters, vol. 10, no. 2, pp. 1154–1161, 2025

    2025

  6. [14]

    Cross-Modal instructions for robot motion generation,

    W. Barron, X. Dong, M. Johnson-Roberson, and W. Zhi, “Cross-Modal instructions for robot motion generation,” in2026 IEEE International Conference on Robotics and Automation, 2026

    2026

  7. [15]

    Dual arm manipulation—a survey,

    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, vol. 60, no. 10, pp. 1340–1353, 2012

    2012

  8. [16]

    Learning multi-arm manipulation through collaborative teleoperation,

    A. Tung, J. Wong, A. Mandlekar, R. Mart ´ın-Mart´ın, Y . Zhu, L. Fei- Fei, and S. Savarese, “Learning multi-arm manipulation through collaborative teleoperation,” in2021 IEEE International Conference on Robotics and Automation, 2021, pp. 9212–9219

    2021

  9. [17]

    Tri- manipulation: An evaluation of human performance in 3-handed teleoperation,

    Y . Huang, J. Eden, L. Cao, E. Burdet, and S. J. Phee, “Tri- manipulation: An evaluation of human performance in 3-handed teleoperation,”IEEE Transactions on Medical Robotics and Bionics, vol. 2, no. 4, pp. 545–548, 2020

    2020

  10. [18]

    Can training make three arms better than two heads for trimanual coordination?

    Y . Huang, J. Eden, E. Ivanova, and E. Burdet, “Can training make three arms better than two heads for trimanual coordination?”IEEE Open Journal of Engineering in Medicine and Biology, vol. 4, pp. 148–155, 2023

    2023

  11. [19]

    Review of research and development of super- numerary robotic limbs,

    Y . Tong and J. Liu, “Review of research and development of super- numerary robotic limbs,”IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 5, pp. 929–952, 2021

    2021

  12. [20]

    Robot learning from demonstration by constructing skill trees,

    G. D. Konidaris, S. R. Kuindersma, R. A. Grupen, and A. G. Barto, “Robot learning from demonstration by constructing skill trees,”The International Journal of Robotics Research, vol. 31, no. 3, pp. 360– 375, 2012

    2012

  13. [21]

    A learning from demonstration framework for adaptive task and motion planning in varying package- to-order scenarios,

    R. Ma, J. Chen, and J. Oyekan, “A learning from demonstration framework for adaptive task and motion planning in varying package- to-order scenarios,”Robotics and Computer-Integrated Manufacturing, vol. 82, p. 102539, 2023

    2023

  14. [22]

    TACO: Learning task decomposition via temporal alignment for control,

    K. Shiarlis, M. Wulfmeier, S. Salter, S. Whiteson, and I. Posner, “TACO: Learning task decomposition via temporal alignment for control,” inProceedings of the 35th International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, vol. 80. PMLR, 2018,...

    2018

  15. [23]

    Learning symbolic and subsymbolic tem- poral task constraints from bimanual human demonstrations,

    C. Dreher and T. Asfour, “Learning symbolic and subsymbolic tem- poral task constraints from bimanual human demonstrations,” in2024 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2024, pp. 5160–5167

    2024

  16. [24]

    A constraint programming approach to simultaneous task allocation and motion scheduling for industrial dual-arm manipulation tasks,

    J. K. Behrens, R. Lange, and M. Mansouri, “A constraint programming approach to simultaneous task allocation and motion scheduling for industrial dual-arm manipulation tasks,” in2019 International Confer- ence on Robotics and Automation, 2019, pp. 8705–8711

    2019

  17. [25]

    A general task and motion planning framework for multiple manipulators,

    T. Pan, A. M. Wells, R. Shome, and L. E. Kavraki, “A general task and motion planning framework for multiple manipulators,” in2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021, pp. 3168–3174

    2021

  18. [26]

    Cooperative task and motion planning for multi-arm assembly systems,

    J. Chen, J. Li, Y . Huang, C. Garrett, D. Sun, C. Fan, A. Hofmann, C. Mueller, S. Koenig, and B. C. Williams, “Cooperative task and motion planning for multi-arm assembly systems,” arXiv:2203.02475, 2022

    2022 arXiv

  19. [27]

    HATS: A human-agent teleoperation system for multi-arm data collection,

    Z. Lin, J.-J. Jiang, H. Cen, X.-M. Wu, D. Zhang, and W.-S. Zheng, “HATS: A human-agent teleoperation system for multi-arm data collection,” 2026, arXiv:2606.16491

    2026

  20. [28]

    Robots that collaborate: Sequential asymmetric imitation for learning coupled robot policies,

    Y . Chen, R. Qiu, Z. Li, Y . Zhou, G. Ren, and W. Zhi, “Robots that collaborate: Sequential asymmetric imitation for learning coupled robot policies,” 2026, arXiv:2606.16490

    2026

  21. [29]

    DexImit: Learning bimanual dexterous manipulation from monocular human videos,

    J. Mu, S. Yang, Y . Bao, H. Bae, T. Wei, L. Xu, B. Li, H. Xu, and J. Pang, “DexImit: Learning bimanual dexterous manipulation from monocular human videos,” 2026, arXiv:2602.10105

    2026

  22. [30]

    Unified learning of temporal task structure and action timing for bimanual robot manipulation,

    C. Dreher, P. Dormanns, A. Meixner, and T. Asfour, “Unified learning of temporal task structure and action timing for bimanual robot manipulation,” 2026, arXiv:2603.06538

    2026

  23. [31]

    Proleptic temporal ensemble for improving the speed of robot tasks generated by imitation learning,

    H. Park, D. Lim, S. Kim, and S. Park, “Proleptic temporal ensemble for improving the speed of robot tasks generated by imitation learning,” The Journal of Korea Robotics Society, vol. 20, no. 1, pp. 61–68, 2025

    2025

  24. [32]

    The CP-SAT-LP solver,

    L. Perron, F. Didier, and S. Gay, “The CP-SAT-LP solver,” in29th International Conference on Principles and Practice of Constraint Programming, ser. Leibniz International Proceedings in Informatics, vol. 280. Schloss Dagstuhl–Leibniz-Zentrum f ¨ur Informatik, 2023, pp. 3:1–3:2

    2023

  25. [33]

    SigLIP 2: Multilin- gual vision-language encoders with improved semantic understanding, localization, and dense features,

    M. Tschannen, A. Gritsenko, X. Wang, M. F. Naeem, I. Alabdul- mohsin, N. Parthasarathy, T. Evans, L. Beyer, Y . Xia, B. Mustafa, O. H ´enaff, J. Harmsen, A. Steiner, and X. Zhai, “SigLIP 2: Multilin- gual vision-language encoders with improved semantic understanding, localizat...

    2025 arXiv

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