Pith. sign in

REVIEW 4 major objections 79 references

OmniTacTune lifts weak visual robot policies to high contact-rich success by learning tactile residual corrections in 40–80 minutes of real-world practice.

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

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · grok-4.5

2026-07-12 00:22 UTC pith:7WBPX5ZN

load-bearing objection Solid systems paper: residual tactile RL on frozen visual policies works across four real contact tasks and several base policies; the efficiency claim is real but rests on small-N human-labeled success rates. the 4 major comments →

arxiv 2607.03723 v1 pith:7WBPX5ZN submitted 2026-07-04 cs.RO cs.AI

OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies

Kelin Yu , Haode Zhang , Harish Ravichandar , Yunhai Han , Ruohan Gao This is my paper
classification cs.RO cs.AI
keywords visuo-tactile policyreal-world RLcontact-rich manipulationtactile residual adaptationvisual base policiesmulti-sensory rewardresidual RLtactile sensing
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

Vision-only robot policies trained from human video, teleoperation, or demos give scalable motion priors, but they still fail when success hinges on local force and contact geometry that cameras cannot measure. Touch supplies those signals, yet tactile data are scarce and hard to transfer across sensors and tasks. OmniTacTune freezes a pretrained visual policy and adapts tactile feedback as residual correction via real-world RL: a warm-start stage bootstraps a tactile encoder and critic from autonomous base-policy rollouts (with force-conditioned tactile augmentation), then online residual RL learns contact-gated corrections under a multi-sensory reward. On four hardware tasks—peg-in-hole, charger insertion, cap opening, and box opening—it raises success from 5–40% to 85–100% within 40–80 minutes, and the same recipe works across flow, ACT, diffusion, and VLA-style bases and across tactile image and marker representations. A reader who wants scalable vision plus reliable contact cares because this is an efficient path to add touch without collecting large paired visuo-tactile datasets from scratch.

Core claim

OmniTacTune shows that tactile sensing can be adapted to frozen, architecturally diverse visual base policies through residual real-world RL without offline tactile demonstrations. Autonomous base-policy rollouts warm-start a flow-tactile critic and task-adapted tactile encoder; online residual RL then learns lightweight contact-aware corrections on top of the base actions under object-centric multi-sensory reward shaping. Across four contact-rich real-world tasks this raises success from 5–40% to 85–100% in 40–80 minutes and generalizes across base policies and tactile representations.

What carries the argument

Two-stage tactile residual RL: Stage 1 warm-starts a flow-tactile critic and tactile encoder from frozen base-policy rollouts (contact-only encoder updates plus trajectory-level force-conditioned tactile augmentation); Stage 2 freezes the base and trains a residual actor a = a_base + s_t a_residual, gated by contact, conditioned on proprioception, object-centric flow goals, tactile features, and the base action chunk, with SAC under a multi-sensory reward of reaching, grasp, flow-subgoal, and safety terms.

Load-bearing premise

Autonomous rollouts of a still-weak visual policy, plus synthetic tactile augmentation and a hand-designed multi-sensory reward with human success labels, are enough to bootstrap a stable critic and residual policy without offline tactile demos.

What would settle it

On the same four contact-rich tasks and wall-clock budgets, residual training after the paper’s warm-start fails to raise final success above the visual-only residual baseline and above the frozen base across several base-policy architectures and tactile encodings.

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

If this is right

  • Scalable visual policies can be made contact-aware without retraining them or collecting large paired visuo-tactile datasets.
  • Tens of minutes of real-world residual practice can close the last-mile contact gap that pure imitation leaves open.
  • One residual interface can attach to flow, ACT, diffusion, and VLA-style bases, so tactile adaptation need not be architecture-specific.
  • Both compact marker signals and pretrained tactile image encoders can serve as the tactile stream for residual correction.
  • Warm-start of critic and tactile encoder, plus multi-sensory reward shaping, is material to sample-efficient tactile residual RL.

Where Pith is reading between the lines

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

  • If the base policy rarely reaches near-contact states, residual learning can stall; the method implicitly needs a prior that already lands in a useful contact neighborhood.
  • The vision-for-planning, touch-for-refinement split may extend to multi-finger dexterity and bimanual assembly where tactile data remain scarce relative to vision.
  • Manual resets and human terminal success labels remain practical bottlenecks; automating both would be a direct stress test of the same residual recipe.
  • Dynamic levering failures (slip, edge miss, pose tilt) suggest residual scale and force-aware rewards may need richer contact profiles than the current scheduler alone provides.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 0 minor

Summary. OmniTacTune proposes a policy-agnostic two-stage real-world RL pipeline that adapts tactile feedback to frozen visual base policies via residual correction, without offline tactile demonstrations. Stage 1 warm-starts a flow-tactile critic and tactile encoder from autonomous base-policy rollouts (with ControlTac trajectory-level tactile augmentation); Stage 2 learns a lightweight residual actor that adds contact-gated corrections on top of the base action chunk, guided by a multi-sensory reward combining object-centric flow subgoals, tactile grasp/safety terms, and human terminal success labels. On four contact-rich tasks (peg-in-hole, charger insertion, cap opening, box opening), the method reports improving base success from 5–40% to 85–100% in 40–80 minutes, with generalization across base policies (human/teleop flow, ACT, DP, π0.5) and tactile representations (AnyTouch2, Sparsh, T3, markers), outperforming adapted PLD and ViTAL baselines and visuo-tactile imitation under a matched time budget.

Significance. If the reported efficiency and generality hold under stronger evaluation, this is a practically important contribution: it offers a concrete path to attach tactile residual practice to scalable visual priors rather than collecting large paired visuo-tactile datasets. Strengths include real hardware results on four genuinely contact-rich tasks, systematic compatibility tests across policy architectures and tactile encoders, relevant residual-RL and imitation baselines, and ablations of reward components, residual design, warm-start, and action scaling (Sec. 4, App. C). The residual interface (shared flow goals + base action chunks + contact gate) is a clean systems idea for policy-agnostic adaptation. These are falsifiable empirical claims with public project-page materials, not circular constructions.

major comments (4)
  1. Table 1 and Fig. 4 (also Sec. 4.1): the central efficiency claim (5–40% → 85–100% in 40–80 min) rests on final success from 20 trials and intermediate checkpoints from 10 trials, with no variance, confidence intervals, multi-seed runs, or multi-operator evaluation. For Cap Opening and Box Opening the base starts at 5%, so absolute margins over PLD*/ViTAL are large but statistically under-specified. Please report binomial CIs or bootstrap intervals at minimum, and preferably repeated training runs or denser evaluation; without this the headline numbers are not fully secured.
  2. Sec. 3.4 and App. A.6: terminal success is a human-assigned reward of 1, and the same operator performs resets. This couples the learning signal and the evaluation metric. Clarify whether evaluation success uses the same human judgment as training, whether any automatic success detector exists, and how label consistency was controlled across methods. If evaluation is fully human-labeled, consider a blinded protocol or automatic geometric/contact criteria so reported gains cannot be attributed to label drift.
  3. Sec. 3.3–3.4 and the weakest operating regime (5% base policies): residual learning assumes warm-start rollouts plus flow/tactile shaping produce enough near-contact experience for a usable critic. The paper shows warm-start ablations (Fig. 17) but does not quantify contact-state coverage or success of warm-start trajectories on Cap/Box Opening. Please report how often base rollouts reach contact/near-goal states, and discuss failure modes when the base rarely enters the residual’s useful region—this is load-bearing for the claim of no offline tactile demos.
  4. Sec. 4.3 / Table 2: imitation baselines receive 40 extra teleop demos under a 50-minute budget, while OmniTacTune uses online interaction with human terminal labels and dense shaped rewards. The comparison is informative but not fully matched in supervision type. Explicitly state what human effort (resets, success labeling, interventions) is required for OmniTacTune versus teleop collection, so data-efficiency claims are not overstated relative to pure demonstration methods.

Circularity Check

0 steps flagged

No significant circularity: empirical real-world RL systems paper; success rates are measured outcomes, not quantities forced by definition or self-citation.

full rationale

OmniTacTune is a methods-and-experiments robotics paper. Its load-bearing claim is measured task success (5–40% → 85–100% in 40–80 min on four contact-rich tasks; Table 1, Fig. 4), obtained from real robot trials after residual SAC training. That metric is not defined in terms of the reward weights, residual scale schedule, ControlTac force perturbations, or flow subgoals; those are free design choices that could have failed. Warm-start (autonomous base rollouts + critic/encoder bootstrap) and residual action a = a_base + s·a_residual are standard residual-RL constructions, not self-definitional identities that force high success. Self-citations (GenFlowRL/Im2Flow2Act for object flow, ControlTac for tactile augmentation, PLD/ViTAL as baselines) supply components or comparison points; they do not import a uniqueness theorem or rename a known empirical law as a new prediction. There is no fitted constant re-presented as a first-principles forecast, and no derivation chain that reduces the headline result to its inputs by construction. Evaluation sparsity and human terminal labels are statistical/correctness concerns, not circularity.

Axiom & Free-Parameter Ledger

4 free parameters · 4 axioms · 2 invented entities

The central empirical claim rests on standard RL machinery plus several hand-chosen reward/contact thresholds, residual scales, and the assumption that base-policy rollouts plus synthetic tactile augmentation yield a usable critic. No new physical entities are postulated; invented constructs are algorithmic modules.

free parameters (4)
  • Reward weights (wr, wg, wf, ws) = 1.0 / 0.5 / 2.0 / 1.0
    Fixed to 1.0, 0.5, 2.0, 1.0 for all tasks; shape the multi-sensory objective that drives residual learning.
  • Residual action scale schedule st = 0.05 to 0.15
    Scheduled 0.05→0.10→0.15; controls exploration safety and final correction magnitude.
  • Contact/safety thresholds (εcontact, εsafety, εdepth, εflow) = e.g. 1.5 px, 8.0 px, 0.10, 0.03
    Hand-set pixel/depth/flow thresholds gate tactile input, grasp reward, safety resets, and subgoal switching.
  • Warm-start duration and ControlTac force range = 12 min; Δf in [-3,10]
    12 min warm-start; Δf ~ U[-3,10] for trajectory-level tactile augmentation—design choices affecting critic bootstrap quality.
axioms (4)
  • domain assumption Frozen visual base policies provide useful near-contact motion priors that residual tactile corrections can refine rather than replace.
    Load-bearing for residual formulation a = abase + s·aresidual (Sec. 3.3); if bases never approach contact, residual RL cannot practice usefully.
  • domain assumption Object-centric keypoint flow from a fine-tuned generator yields a dense, embodiment-agnostic reward and shared residual conditioning across policy architectures.
    Used for both base flow policy and multi-sensory reward (Sec. 3.2, 3.4); failures of flow generation would degrade guidance.
  • domain assumption SAC with twin critics, contact-gated tactile features, and reconstruction-regularized encoder adaptation is a valid online learner for residual actions on hardware.
    Standard RL assumption adapted to real-world residual setting (Sec. 3.3, App. A.5).
  • ad hoc to paper Marker displacement / tactile depth thresholds detect contact and unsafe force reliably enough for gating and safety resets.
    Thresholds are paper-specific engineering choices (App. A.6) that structure when tactile enters the residual policy.
invented entities (2)
  • OmniTacTune two-stage tactile residual RL pipeline no independent evidence
    purpose: Warm-start tactile encoder/critic from base rollouts, then learn residual corrections online without offline tactile demos.
    Core algorithmic construct of the paper; evaluated empirically, not a physical entity.
  • Object-centric multi-sensory reward (flow + tactile grasp/safety) no independent evidence
    purpose: Provide dense real-world RL signal combining generated object motion and contact quality.
    Composite reward design specific to this pipeline; ablated but not independently validated outside the paper’s tasks.

pith-pipeline@v1.1.0-grok45 · 32030 in / 3294 out tokens · 28474 ms · 2026-07-12T00:22:07.471648+00:00 · methodology

0 comments
read the original abstract

Visual policies learned from human videos, teleoperation, and robot demonstrations offer scalable motion priors, but often fail in contact-rich manipulation, where success significantly depends on local force and contact geometry. Tactile sensing provides these complementary signals, yet tactile data remain costly to collect and hard to generalize across sensors, robots, and tasks. We introduce OmniTacTune, a policy-agnostic real-world RL pipeline that adapts tactile feedback to pretrained visual policies through residual correction. OmniTacTune uses a two-stage design: it first bootstraps tactile-aware learning from autonomous base-policy rollouts, then learns a lightweight tactile residual policy through online interaction. Extensive experiments show that OmniTacTune generalizes across diverse contact-rich tasks, visual base policies, and tactile representations. Across four real-world contact-rich tasks, it improves visual base policies from 5-40% success to 85-100% within 40-80 minutes, demonstrating an efficient path for adapting tactile feedback to scalable visual robot policies. Project page: https://colinyu1.github.io/omnitactune-site/

Figures

Figures reproduced from arXiv: 2607.03723 by Haode Zhang, Harish Ravichandar, Kelin Yu, Ruohan Gao, Yunhai Han.

Figure 1
Figure 1. Figure 1: OMNITACTUNE adapts tactile feedback to diverse visual base policies trained from hu￾man videos or robot data (left) through a two-stage real-world tactile residual RL pipeline (middle), enabling efficient tactile adaptation across challenging contact-rich manipulation tasks (right). 1 Introduction Visual robot policy learning has made tremendous progress, driven in large part by scaling. The field is movin… view at source ↗
Figure 2
Figure 2. Figure 2: System overview of OMNITACTUNE. OMNITACTUNE first collects visual demonstra￾tions via human video retargeting and VR teleoperation, and uses them to train diverse visual base policies (left). It then adapts tactile feedback through a two-stage real-world residual RL pipeline: a warm-start stage collects tactile rollouts to optimize the tactile encoder and critic, followed by an online policy learning stage… view at source ↗
Figure 3
Figure 3. Figure 3: Real-world contact-rich manipulation tasks. We evaluate OMNITACTUNE on four tasks: peg-in-hole requires spatial generalization and multi-stage insertion; charger insertion re￾quires contact exploration and precise insertion; cap opening requires dynamic contact reasoning and pose adjustment; and box opening requires precise edge alignment and contact-rich opening [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of RL training results across four different contact-rich manipulation tasks. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: OMNITACTUNE consistently im￾proves diverse base visual policies. 7 [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Adapting different tactile rep [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Visualization of our data collection system, for both hand and finger retargeting. [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Generated flows for each tasks. backbone frozen. This improves task-specific flow generation while avoiding overfitting to the small demonstration set. The generator is trained with a point-wise flow prediction loss and a temporal smoothness regular￾izer: Lflow = 1 T N X T t=1 X N i=1 [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Visualization of ControlTac Generation Process. [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Setting of Peg-in-Hole task. Charger Insertion Task. The robot has been grasped a charger and plan to insert it into a phone charging port. This task is substantially more precise than standard insertion because both the charger head and the socket tolerance are very small. As shown in [PITH_FULL_IMAGE:figures/full_fig_p023_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Setting of Charger Insertion task. the port but cannot reliably resolve the final contact alignment. The robot must use tactile feedback to explore contact, detect lateral misalignment, and make small corrective motions before insertion. To improve robustness, we apply small domain randomization for the initial robot position during both training and inference by perturbing the initial pose within 5 cm al… view at source ↗
Figure 12
Figure 12. Figure 12: Setting of Cap Opening task. we apply small domain randomization for the initial robot position during both training and inference by perturbing the initial pose within 5 cm along the x, y, and z directions and within 10◦ around the vertical axis. The setting is visualized in [PITH_FULL_IMAGE:figures/full_fig_p024_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Setting of Box Opening task. C Additional Experiments C.1 Analysis of Collected Data and Base Policies In this section, we want to analyze the trajectory quality of the base policies and the data. To evaluate their quality, we propose to use the smoothness of the tracked keypoints centroid on the object, since all sources share this representation. Following prior work [75], we use two complementary smoot… view at source ↗
Figure 14
Figure 14. Figure 14: Visualization of centroid trajectories [PITH_FULL_IMAGE:figures/full_fig_p026_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Ablation Study of the Multi-sensory Reward Shaping Residual Policy Design We further ablate our residual pol￾icy design in [PITH_FULL_IMAGE:figures/full_fig_p026_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Ablation between our residual policy design and per-step keypoint reaching policy, visuo￾tactile policy, or policy after re￾moving contact-aware gating. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: , removing warm-start optimization on the tactile en￾coder and critic leads to unstable early stage and weak overall performance, which indicates that optimizing the tactile rep￾resentation and critic during warm-start is important for stable residual RL. We also observe that removing ControlTac [66] also results in unstable performance, highlighting that tactile augmentation in the warm-start stage can r… view at source ↗
Figure 18
Figure 18. Figure 18: Ablation study of the action space in showing the im￾portance of scheduler and the scale number. D Failure Cases In this section, we further analyze representative failure cases in the Cap Opening and Box Opening tasks, as shown in [PITH_FULL_IMAGE:figures/full_fig_p027_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Visualization of the failure cases on Cap Opening and Box Opening. E Additional plots In this section, we visualize the training curve of the success rates across different visual base poli￾cies and tactile representations, which are additional results of Sec. 4.2 and Sec. 4.4. The plots are shown in [PITH_FULL_IMAGE:figures/full_fig_p028_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: The training success rates for different policies, which is shown in Sec. [PITH_FULL_IMAGE:figures/full_fig_p029_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: The training success rates for different policies, which is shown in Sec. [PITH_FULL_IMAGE:figures/full_fig_p029_21.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

79 extracted references · 50 linked inside Pith

  1. [1]

    Collaboration, A

    E. Collaboration, A. O’Neill, A. Rehman, A. Gupta, A. Maddukuri, A. Gupta, A. Padalkar, A. Lee, A. Pooley, A. Gupta, A. Mandlekar, A. Jain, A. Tung, A. Bewley, A. Herzog, A. Ir- pan, A. Khazatsky, A. Rai, A. Gupta, A. Wang, A. Kolobov, A. Singh, A. Garg, A. Kembhavi, A. Xie, A. Brohan, A. Raffin, A. Sharma, A. Yavary, A. Jain, A. Balakrishna, A. Wahid, B....

    Pith/arXiv arXiv 2025

  2. [2]

    Khazatsky, K

    A. Khazatsky, K. Pertsch, S. Nair, A. Balakrishna, S. Dasari, S. Karamcheti, S. Nasiriany, M. K. Srirama, L. Y . Chen, K. Ellis, P. D. Fagan, J. Hejna, M. Itkina, M. Lepert, Y . J. Ma, P. T. Miller, J. Wu, S. Belkhale, S. Dass, H. Ha, A. Jain, A. Lee, Y . Lee, M. Memmel, S. Park, I. Radosavovic, K. Wang, A. Zhan, K. Black, C. Chi, K. B. Hatch, S. Lin, J. ...

    Pith/arXiv arXiv 2025

  3. [3]

    Kareer, D

    S. Kareer, D. Patel, R. Punamiya, P. Mathur, S. Cheng, C. Wang, J. Hoffman, and D. Xu. Egomimic: Scaling imitation learning via egocentric video, 2024. URLhttps://arxiv. org/abs/2410.24221

    Pith/arXiv arXiv 2024

  4. [4]

    Zheng, D

    R. Zheng, D. Niu, Y . Xie, J. Wang, M. Xu, Y . Jiang, F. Casta ˜neda, F. Hu, Y . L. Tan, L. Fu, T. Darrell, F. Huang, Y . Zhu, D. Xu, and L. Fan. Egoscale: Scaling dexterous manipulation with diverse egocentric human data, 2026. URLhttps://arxiv.org/abs/2602.16710

    arXiv 2026

  5. [5]

    Punamiya, S

    R. Punamiya, S. Kareer, Z. Liu, J. Citron, R.-Z. Qiu, X. Cai, A. Gavryushin, J. Chen, D. Li- conti, L. Y . Zhu, P. Aphiwetsa, B. Li, A. Cheluva, P. Kuppili, Y . Liu, D. Patel, A. Gao, H.-Y . Chung, R. Co, R. Zbizika, J. Liu, X. Xu, H. Xiong, G. Chen, S. Oliani, C. Yang, X. Wang, J. Fort, R. Newcombe, J. Gao, J. Chong, G. Matsuda, A. Doriwala, M. Pollefeys...

    Pith/arXiv arXiv 2026

  6. [6]

    W. Yuan, S. Dong, and E. H. Adelson. Gelsight: High-resolution robot tactile sensors for estimating geometry and force.Sensors, 17(12):2762, 2017

    2017

  7. [7]

    K. Yu, Y . Han, Q. Wang, V . Saxena, D. Xu, and Y . Zhao. Mimictouch: Leveraging multi- modal human tactile demonstrations for contact-rich manipulation, 2025. URLhttps:// arxiv.org/abs/2310.16917

    Pith/arXiv arXiv 2025

  8. [8]

    Huang, Y

    B. Huang, Y . Wang, X. Yang, Y . Luo, and Y . Li. 3d-vitac: Learning fine-grained manipulation with visuo-tactile sensing, 2025. URLhttps://arxiv.org/abs/2410.24091

    Pith/arXiv arXiv 2025

  9. [9]

    H. Li, Y . Zhang, J. Zhu, S. Wang, M. A. Lee, H. Xu, E. Adelson, L. Fei-Fei, R. Gao, and J. Wu. See, hear, and feel: Smart sensory fusion for robotic manipulation, 2022. URLhttps: //arxiv.org/abs/2212.03858

    Pith/arXiv arXiv 2022

  10. [10]

    H. Xue, J. Ren, W. Chen, G. Zhang, Y . Fang, G. Gu, H. Xu, and C. Lu. Reactive diffu- sion policy: Slow-fast visual-tactile policy learning for contact-rich manipulation, 2025. URL https://arxiv.org/abs/2503.02881

    Pith/arXiv arXiv 2025

  11. [11]

    Y . Dou, F. Yang, Y . Liu, A. Loquercio, and A. Owens. Tactile-augmented radiance fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26529–26539, 2024

    2024

  12. [12]

    X. Zhu, B. Huang, and Y . Li. Touch in the wild: Learning fine-grained manipulation with a portable visuo-tactile gripper. InThe Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025. URLhttps://openreview.net/forum?id=WabVVQKTUF

    2025

  13. [13]

    L. Fu, G. Datta, H. Huang, W. C.-H. Panitch, J. Drake, J. Ortiz, M. Mukadam, M. Lam- beta, R. Calandra, and K. Goldberg. A touch, vision, and language dataset for multi- modal alignment. InForty-first International Conference on Machine Learning, 2024. URL https://openreview.net/forum?id=tFEOOH9eH0

    2024

  14. [14]

    R. Gao, Y . Dou, H. Li, T. Agarwal, J. Bohg, Y . Li, L. Fei-Fei, and J. Wu. The object- folder benchmark: Multisensory learning with neural and real objects. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17276–17286, 2023

    2023

  15. [15]

    F. Liu, C. Li, Y . Qin, J. Xu, P. Abbeel, and R. Chen. Vitamin: Learning contact-rich tasks through robot-free visuo-tactile manipulation interface, 2025. URLhttps://arxiv.org/ abs/2504.06156

    Pith/arXiv arXiv 2025

  16. [16]

    Hoque, P

    R. Hoque, P. Huang, D. J. Yoon, M. Sivapurapu, and J. Zhang. Egodex: Learning dexter- ous manipulation from large-scale egocentric video, 2026. URLhttps://arxiv.org/abs/ 2505.11709. 10

    Pith/arXiv arXiv 2026

  17. [17]

    Huang, J

    B. Huang, J. Xu, I. Akinola, W. Yang, B. Sundaralingam, R. O’Flaherty, D. Fox, X. Wang, A. Mousavian, Y .-W. Chao, and Y . Li. VT-refine: Learning bimanual assembly with visuo- tactile feedback via simulation fine-tuning. In9th Annual Conference on Robot Learning,

  18. [18]

    URLhttps://openreview.net/forum?id=bOVF8Rj33i

  19. [19]

    P. Dan, K. Kedia, A. Chao, E. W. Duan, M. A. Pace, W.-C. Ma, and S. Choudhury. X-sim: Cross-embodiment learning via real-to-sim-to-real, 2025. URLhttps://arxiv.org/abs/ 2505.07096

    arXiv 2025

  20. [20]

    W. Wan, J. Fu, X. Yuan, Y . Zhu, and H. Su. Lodestar: Long-horizon dexterity via synthetic data augmentation from human demonstrations, 2025. URLhttps://arxiv.org/abs/2508. 17547

    2025

  21. [21]

    S. Ross, G. J. Gordon, and J. A. Bagnell. A reduction of imitation learning and structured prediction to no-regret online learning, 2011. URLhttps://arxiv.org/abs/1011.0686

    Pith/arXiv arXiv 2011

  22. [22]

    J. Luo, C. Xu, J. Wu, and S. Levine. Precise and dexterous robotic manipulation via human- in-the-loop reinforcement learning, 2025. URLhttps://arxiv.org/abs/2410.21845

    Pith/arXiv arXiv 2025

  23. [23]

    W. Xiao, H. Lin, A. Peng, H. Xue, T. He, Y . Xie, F. Hu, J. Wu, Z. Luo, L. J. Fan, G. Shi, and Y . Zhu. Self-improving vision-language-action models with data generation via residual rl, 2025. URLhttps://arxiv.org/abs/2511.00091

    arXiv 2025

  24. [24]

    Z. Zhou, A. Peng, Q. Li, S. Levine, and A. Kumar. Efficient online reinforcement learning fine-tuning need not retain offline data, 2025. URLhttps://arxiv.org/abs/2412.07762

    Pith/arXiv arXiv 2025

  25. [25]

    C. Xu, J. T. Springenberg, M. Equi, A. Amin, A. Esmail, S. Levine, and L. Ke. Rl token: Bootstrapping online rl with vision-language-action models, 2026. URLhttps://arxiv. org/abs/2604.23073

    Pith/arXiv arXiv 2026

  26. [26]

    T. Z. Zhao, V . Kumar, S. Levine, and C. Finn. Learning fine-grained bimanual manipulation with low-cost hardware, 2023. URLhttps://arxiv.org/abs/2304.13705

    Pith/arXiv arXiv 2023

  27. [27]

    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, 44(10-11):1684–1704, 2025

    2025

  28. [28]

    M. Xu, Z. Xu, Y . Xu, C. Chi, G. Wetzstein, M. Veloso, and S. Song. Flow as the cross-domain manipulation interface, 2024. URLhttps://arxiv.org/abs/2407.15208

    Pith/arXiv arXiv 2024

  29. [29]

    Intelligence, K

    P. Intelligence, K. Black, N. Brown, J. Darpinian, K. Dhabalia, D. Driess, A. Esmail, M. Equi, C. Finn, N. Fusai, M. Y . Galliker, D. Ghosh, L. Groom, K. Hausman, B. Ichter, S. Jakubczak, T. Jones, L. Ke, D. LeBlanc, S. Levine, A. Li-Bell, M. Mothukuri, S. Nair, K. Pertsch, A. Z. Ren, L. X. Shi, L. Smith, J. T. Springenberg, K. Stachowicz, J. Tanner, Q. V...

    Pith/arXiv arXiv 2025

  30. [30]

    Intelligence, A

    P. Intelligence, A. Amin, R. Aniceto, A. Balakrishna, K. Black, K. Conley, G. Connors, J. Darpinian, K. Dhabalia, J. DiCarlo, D. Driess, M. Equi, A. Esmail, Y . Fang, C. Finn, C. Glos- sop, T. Godden, I. Goryachev, L. Groom, H. Hancock, K. Hausman, G. Hussein, B. Ichter, S. Jakubczak, R. Jen, T. Jones, B. Katz, L. Ke, C. Kuchi, M. Lamb, D. LeBlanc, S. Lev...

    Pith/arXiv arXiv 2025

  31. [31]

    M. J. Kim, K. Pertsch, S. Karamcheti, T. Xiao, A. Balakrishna, S. Nair, R. Rafailov, E. Foster, G. Lam, P. Sanketi, Q. Vuong, T. Kollar, B. Burchfiel, R. Tedrake, D. Sadigh, S. Levine, P. Liang, and C. Finn. Openvla: An open-source vision-language-action model, 2024. URL https://arxiv.org/abs/2406.09246. 11

    Pith/arXiv arXiv 2024

  32. [32]

    L. Y . Zhu, P. Kuppili, R. Punamiya, P. Aphiwetsa, D. Patel, S. Kareer, S. Ha, and D. Xu. Emma: Scaling mobile manipulation via egocentric human data, 2025. URLhttps://arxiv.org/ abs/2509.04443

    arXiv 2025

  33. [33]

    Y . Liu, W. C. Shin, Y . Han, Z. Chen, H. Ravichandar, and D. Xu. Immimic: Cross-domain imitation from human videos via mapping and interpolation, 2025. URLhttps://arxiv. org/abs/2509.10952

    Pith/arXiv arXiv 2025

  34. [34]

    K. Yu, S. Zhang, H. Soora, F. Huang, H. Huang, P. Tokekar, and R. Gao. Genflowrl: Shap- ing rewards with generative object-centric flow in visual reinforcement learning, 2025. URL https://arxiv.org/abs/2508.11049

    Pith/arXiv arXiv 2025

  35. [35]

    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

  36. [36]

    Z. Wang, B. He, K. Yu, S. Lee, R. Gao, F. Huang, and Y . Aloimonos. Humanego: Zero-shot robot learning from minutes of human egocentric videos, 2026

    2026

  37. [37]

    Huang, C

    W. Huang, C. Wang, Y . Li, R. Zhang, and L. Fei-Fei. Rekep: Spatio-temporal reasoning of relational keypoint constraints for robotic manipulation.arXiv preprint arXiv:2409.01652, 2024

    Pith/arXiv arXiv 2024

  38. [38]

    Singh, K

    A. Singh, K. Torshizi, K. Habib, K. Yu, R. Gao, and P. Tokekar. Afford2act: Affordance- guided automatic keypoint selection for generalizable and lightweight robotic manipulation,

  39. [39]

    URLhttps://arxiv.org/abs/2510.01433

    Pith/arXiv arXiv

  40. [40]

    S. Bahl, A. Gupta, and D. Pathak. Human-to-robot imitation in the wild, 2022. URLhttps: //arxiv.org/abs/2207.09450

    Pith/arXiv arXiv 2022

  41. [41]

    Bhirangi, V

    R. Bhirangi, V . Pattabiraman, E. Erciyes, Y . Cao, T. Hellebrekers, and L. Pinto. Anyskin: Plug- and-play skin sensing for robotic touch, 2024. URLhttps://arxiv.org/abs/2409.08276

    Pith/arXiv arXiv 2024

  42. [42]

    Lambeta, P.-W

    M. Lambeta, P.-W. Chou, S. Tian, B. Yang, B. Maloon, V . R. Most, D. Stroud, R. Santos, A. Byagowi, G. Kammerer, D. Jayaraman, and R. Calandra. Digit: A novel design for a low- cost compact high-resolution tactile sensor with application to in-hand manipulation.IEEE Robotics and Automation Letters, 5(3):3838–3845, 2020. ISSN 2377-3774. doi:10.1109/lra. 20...

    doi:10.1109/lra 2020

  43. [43]

    Kim and A

    S. Kim and A. Rodriguez. Active extrinsic contact sensing: Application to general peg-in-hole insertion, 2022. URLhttps://arxiv.org/abs/2110.03555

    Pith/arXiv arXiv 2022

  44. [44]

    Y . Han, K. Yu, R. Batra, N. Boyd, C. Mehta, T. Zhao, Y . She, S. Hutchinson, and Y . Zhao. Learning generalizable vision-tactile robotic grasping strategy for deformable objects via trans- former.IEEE/ASME Transactions on Mechatronics, 30(1):554–566, 2025. doi:10.1109/ TMECH.2024.3400789

    arXiv 2025

  45. [45]

    Xu and Y

    Z. Xu and Y . She. Letac-mpc: Learning model predictive control for tactile-reactive grasping,

  46. [46]

    URLhttps://arxiv.org/abs/2403.04934

    Pith/arXiv arXiv

  47. [47]

    Z.-H. Yin, B. Huang, Y . Qin, Q. Chen, and X. Wang. Rotating without seeing: Towards in-hand dexterity through touch, 2023. URLhttps://arxiv.org/abs/2303.10880

    Pith/arXiv arXiv 2023

  48. [48]

    Guzey, B

    I. Guzey, B. Evans, S. Chintala, and L. Pinto. Dexterity from touch: Self-supervised pre- training of tactile representations with robotic play, 2023. URLhttps://arxiv.org/abs/ 2303.12076

    Pith/arXiv arXiv 2023

  49. [49]

    J. J. Liu, Y . Li, K. Shaw, T. Tao, R. Salakhutdinov, and D. Pathak. Factr: Force-attending curriculum training for contact-rich policy learning, 2025. URLhttps://arxiv.org/abs/ 2502.17432. 12

    Pith/arXiv arXiv 2025

  50. [50]

    Sferrazza, Y

    C. Sferrazza, Y . Seo, H. Liu, Y . Lee, and P. Abbeel. The power of the senses: Generalizable manipulation from vision and touch through masked multimodal learning, 2023. URLhttps: //arxiv.org/abs/2311.00924

    Pith/arXiv arXiv 2023

  51. [51]

    Kelly, C

    M. Kelly, C. Sidrane, K. Driggs-Campbell, and M. J. Kochenderfer. Hg-dagger: Interactive imitation learning with human experts, 2019. URLhttps://arxiv.org/abs/1810.02890

    Pith/arXiv arXiv 2019

  52. [52]

    X. Xu, Y . Hou, Z. Liu, and S. Song. Compliant residual DAgger: Improving real-world contact- rich manipulation with human corrections. InThe Thirty-ninth Annual Conference on Neural Information Processing Systems (NeurIPS), 2025. URLhttps://openreview.net/forum? id=cjcm5LYVWm

    2025

  53. [53]

    Z. Chen, S. Chen, E. Arlaud, I. Laptev, and C. Schmid. Vividex: Learning vision-based dexter- ous manipulation from human videos, 2025. URLhttps://arxiv.org/abs/2404.15709

    Pith/arXiv arXiv 2025

  54. [54]

    Levine, A

    S. Levine, A. Kumar, G. Tucker, and J. Fu. Offline reinforcement learning: Tutorial, review, and perspectives on open problems, 2020. URLhttps://arxiv.org/abs/2005.01643

    Pith/arXiv arXiv 2020

  55. [55]

    Kostrikov, A

    I. Kostrikov, A. Nair, and S. Levine. Offline reinforcement learning with implicit q-learning,

  56. [56]

    URLhttps://arxiv.org/abs/2110.06169

    Pith/arXiv arXiv

  57. [57]

    Nakamoto, Y

    M. Nakamoto, Y . Zhai, A. Singh, M. S. Mark, Y . Ma, C. Finn, A. Kumar, and S. Levine. Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning, 2024. URLhttps: //arxiv.org/abs/2303.05479

    Pith/arXiv arXiv 2024

  58. [58]

    P. J. Ball, L. Smith, I. Kostrikov, and S. Levine. Efficient online reinforcement learning with offline data, 2023. URLhttps://arxiv.org/abs/2302.02948

    Pith/arXiv arXiv 2023

  59. [59]

    Ankile, A

    L. Ankile, A. Simeonov, I. Shenfeld, M. Torne, and P. Agrawal. From imitation to refinement – residual rl for precise assembly, 2024. URLhttps://arxiv.org/abs/2407.16677

    Pith/arXiv arXiv 2024

  60. [60]

    Haldar, J

    S. Haldar, J. Pari, A. Rai, and L. Pinto. Teach a robot to fish: Versatile imitation from one minute of demonstrations, 2023. URLhttps://arxiv.org/abs/2303.01497

    Pith/arXiv arXiv 2023

  61. [61]

    Guzey, Y

    I. Guzey, Y . Dai, G. Savva, R. Bhirangi, and L. Pinto. Bridging the human to robot dexterity gap through object-oriented rewards, 2024. URLhttps://arxiv.org/abs/2410.23289

    Pith/arXiv arXiv 2024

  62. [62]

    A. Iyer, Z. Peng, Y . Dai, I. Guzey, S. Haldar, S. Chintala, and L. Pinto. Open teach: A versatile teleoperation system for robotic manipulation, 2024. URLhttps://arxiv.org/abs/2403. 07870

    2024

  63. [63]

    Kuang, S

    Y . Kuang, S. Park, K. Fragkiadaki, and S. Tulsiani. Dex4d: Task-agnostic point track policy for sim-to-real dexterous manipulation, 2026. URLhttps://arxiv.org/abs/2602.15828

    arXiv 2026

  64. [64]

    Oquab, T

    M. Oquab, T. Darcet, T. Moutakanni, H. V o, M. Szafraniec, V . Khalidov, P. Fernandez, D. Haz- iza, F. Massa, A. El-Nouby, M. Assran, N. Ballas, W. Galuba, R. Howes, P.-Y . Huang, S.-W. Li, I. Misra, M. Rabbat, V . Sharma, G. Synnaeve, H. Xu, H. Jegou, J. Mairal, P. Labatut, A. Joulin, and P. Bojanowski. Dinov2: Learning robust visual features without sup...

    Pith/arXiv arXiv 2024

  65. [65]

    Kirillov, E

    A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y . Lo, P. Doll ´ar, and R. Girshick. Segment anything, 2023. URLhttps: //arxiv.org/abs/2304.02643

    Pith/arXiv arXiv 2023

  66. [66]

    Karaev, I

    N. Karaev, I. Makarov, J. Wang, N. Neverova, A. Vedaldi, and C. Rupprecht. Cotracker3: Simpler and better point tracking by pseudo-labelling real videos, 2024. URLhttps: //arxiv.org/abs/2410.11831. 13

    Pith/arXiv arXiv 2024

  67. [67]

    Z. Zhao, S. Haldar, J. Cui, L. Pinto, and R. Bhirangi. Touch begins where vision ends: General- izable policies for contact-rich manipulation, 2025. URLhttps://arxiv.org/abs/2506. 13762

    2025

  68. [68]

    R. Feng, Y . Zhou, S. Mei, D. Zhou, P. Wang, S. Cui, B. Fang, G. Yao, and D. Hu. Anytouch 2: General optical tactile representation learning for dynamic tactile perception, 2026. URL https://arxiv.org/abs/2602.09617

    arXiv 2026

  69. [69]

    Yarats, R

    D. Yarats, R. Fergus, A. Lazaric, and L. Pinto. Mastering visual continuous control: Im- proved data-augmented reinforcement learning, 2021. URLhttps://arxiv.org/abs/ 2107.09645

    Pith/arXiv arXiv 2021

  70. [70]

    D. Luo, K. Yu, A.-H. Shahidzadeh, C. Ferm ¨uller, Y . Aloimonos, and R. Gao. Controltac: Force- and position-controlled tactile data augmentation with a single reference image, 2025. URLhttps://arxiv.org/abs/2505.20498

    Pith/arXiv arXiv 2025

  71. [71]

    Huang and Y

    B. Huang and Y . Li. Flexitac: A low-cost, open-source, scalable tactile sensing solution for robotic systems, 2026. URLhttps://arxiv.org/abs/2604.28156

    Pith/arXiv arXiv 2026

  72. [72]

    Higuera, A

    C. Higuera, A. Sharma, C. K. Bodduluri, T. Fan, P. Lancaster, M. Kalakrishnan, M. Kaess, B. Boots, M. Lambeta, T. Wu, and M. Mukadam. Sparsh: Self-supervised touch representa- tions for vision-based tactile sensing, 2024. URLhttps://arxiv.org/abs/2410.24090

    Pith/arXiv arXiv 2024

  73. [73]

    J. Zhao, Y . Ma, L. Wang, and E. H. Adelson. Transferable tactile transformers for representa- tion learning across diverse sensors and tasks, 2024. URLhttps://arxiv.org/abs/2406. 13640

    2024

  74. [74]

    Y . Guo, T. Lee, L. X. Shi, J. Chen, P. Liang, and C. Finn. Vlaw: Iterative co-improvement of vision-language-action policy and world model, 2026. URLhttps://arxiv.org/abs/ 2602.12063

    arXiv 2026

  75. [75]

    S. Yang, Y . Du, K. Ghasemipour, J. Tompson, L. Kaelbling, D. Schuurmans, and P. Abbeel. Learning interactive real-world simulators, 2024. URLhttps://arxiv.org/abs/2310. 06114

    2024

  76. [76]

    Hafner, J

    D. Hafner, J. Pasukonis, J. Ba, and T. Lillicrap. Mastering diverse domains through world models, 2024. URLhttps://arxiv.org/abs/2301.04104

    Pith/arXiv arXiv 2024

  77. [77]

    E. J. Hu, Y . Shen, P. Wallis, Z. Allen-Zhu, Y . Li, S. Wang, L. Wang, and W. Chen. Lora: Low- rank adaptation of large language models, 2021. URLhttps://arxiv.org/abs/2106. 09685

    2021

  78. [78]

    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. Zakka, and T. Z. Zhao. Aloha 2: An enhanced low-cost hardware for bimanual teleo...

    Pith/arXiv arXiv 2024

  79. [79]

    insert the peg into the hole

    N. R. Arachchige, Z. Chen, W. Jung, W. C. Shin, R. Bansal, P. Barroso, Y . H. He, Y . C. Lin, B. Joffe, S. Kousik, and D. Xu. Sail: Faster-than-demonstration execution of imitation learning policies, 2025. URLhttps://arxiv.org/abs/2506.11948. 14 Appendix A Implementation Details Data Collection System Finger Retargeting Figure 7: Visualization of our data...

    Pith/arXiv arXiv 2025