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REVIEW 4 major objections 6 minor 35 references

Feel the Force: Contact-Driven Learning from Humans

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A tactile glove can teach a robot to apply human-level contact forces, hitting 77% on delicate tasks.

desk verdict A real system for learning force-sensitive manipulation from human tactile demonstrations, with a genuine but unvalidated sensor-comparability assumption that a referee should push on. read the letter →

arxiv 2506.01944 v1 pith:FYT3TH5Z submitted 2025-06-02 cs.RO cs.AI

Ademi Adeniji , Zhuoran Chen , Vincent Liu , Venkatesh Pattabiraman , Raunaq Bhirangi , Siddhant Haldar , Pieter Abbeel , Lerrel Pinto This is my paper
classification cs.ROcs.AI MSC 68T4068T05
keywords tactileimitationlearningcontact-forcepredictionhumandemonstrationsembodimenttransferPDforcecontrolforce-sensitivemanipulationvision-basedhandposeAnySkinsensing
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

FeelTheForce (FTF) claims that contact-rich robot manipulation can be learned directly from human demonstrations recorded with a tactile glove and ordinary cameras, without any robot-collected data. The key move is to train a policy that predicts not only where the robot should move but also how much force to apply at each step, then let a PD controller close the gripper until the measured contact force matches the prediction. The paper reports that this active force-prediction scheme succeeds on 77% of trials across five force-sensitive tasks, while baselines that only feed force readings to the policy fail on most tasks. If true, this means everyday human hand behavior can substitute for expensive haptic teleoperation in teaching robots fine-grained force control.

What carries the argument

The load-bearing piece is an inference-time force-tracking controller coupled to a keypoint-based representation that is shared between the human and the robot. Hand motion is turned into 3D point tracks by triangulating vision-based hand keypoints from two calibrated cameras; object points come from a sparse human annotation, propagated by a semantic correspondence model and followed by a point tracker. A transformer policy consumes these point tracks plus gripper state and force and outputs future robot point tracks, gripper open/close decisions, and a desired contact force $\hat{F}_t$. At execution, a PD controller closes the loop on force: it reads the AnySkin sensor value $F_t^\tau$, updates gripper closure by $g_t^{\tau+1}=g_t^\tau+k(\hat{F}_t-F_t^\tau)$, and repeats until $|\hat{F}_t-F_t^\tau|<\epsilon$, with $k=0.001$ and $\epsilon=5$ for all tasks. That split between where to move and how hard to press is what lets the robot adapt to sensor noise and to the difference between a human hand and a two-finger gripper.

What would settle it

Mount one AnySkin sensor on the glove and an identical sensor on the robot gripper, press both against the same weighing scale at matched loads from 0 to 5 N across the contact modes shown in Figure 6, and compare the mapped Newton values. If the two platforms disagree beyond the controller tolerance at any shared load, the predicted force setpoints would drive the gripper to the wrong closure.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that modeling human tactile behavior as a stream of predicted force setpoints makes zero-shot human-to-robot transfer of force-sensitive skills work. From 30 demonstrations per task, a transformer policy consumes 3D keypoint tracks of the hand and objects plus gripper state and force, and outputs future robot point tracks, gripper open/close decisions, and a continuous force prediction $\hat{F}_t$. At execution, an outer-loop PD controller updates gripper closure by $g_t^{\tau+1}=g_t^\tau+k(\hat{F}_t-F_t^\tau)$ until the AnySkin sensor reading converges to $\hat{F}_t$, so the robot continuously stabilizes around the human-derived force profile rather than passively observing tactile inputs. Evaluated on a Franka Panda with a fingertip tactile sensor, FTF solves all five tasks, reaching 13/15 success on three of them and 67% success under adversarial disturbances.

Load-bearing premise

The whole transfer rests on the assumption that a force value read from the glove on a human hand means the same physical thing as a force value read from the robot's fingertip sensor, so the controller's predicted force is a meaningful target for the gripper.

Editorial extensions

If this is right

  • Human tactile demonstrations alone can supply the training signal for force-sensitive robot policies, removing the need for robot teleoperation data or simulation.
  • Predicting force and tracking it with feedback is more effective than passively feeding raw tactile readings into the policy; FTF is the only method that reliably completes the cup-unstacking and deformable-object tasks.
  • The same policy can tolerate test-time tactile distribution shifts: it keeps a 67% success rate when the chip bag is pressed, held, or reoriented during placement.
  • When trained on robot teleoperation data, force prediction still improves performance on most tasks, but the teleoperated force stream is noisier than human glove data, suggesting that the collection method matters.
  • With a one-time camera calibration and sparse object annotations in place, a new task only requires 30 human demonstrations.

Reading between the lines

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

  • Because the controller only needs a force setpoint, this scheme could bolt onto existing visuomotor policies that already output end-effector actions, adding force awareness without retraining the whole stack.
  • Replacing the scalar force norm with directional components would likely extend FTF to shear-dependent skills such as sliding, screwing, or wiping, at the cost of more sensor channels and a more complex tracking law.
  • Relaxing the first-frame pose-matching assumption would allow demonstrations to begin away from the robot reset pose, making the data collection more natural.
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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

4 major / 6 minor

Summary. The paper introduces FeelTheForce (FTF), a system for learning force-sensitive manipulation from human demonstrations. A human wears an AnySkin-equipped glove while performing tasks in front of calibrated cameras; Mediapipe keypoints are triangulated to a point-based action representation, and the glove's force signal is recorded. A transformer policy is trained to predict robot end-effector keypoint tracks, gripper state, and desired contact forces from visual point tracks and force history. At deployment, a Franka Panda with an AnySkin sensor on one jaw executes the predicted end-effector pose while a feedback controller adjusts gripper closure to match the predicted force. The paper reports 58/75 (77%) successes across five tasks, with binary/continuous-gripper baselines mostly failing, and reports 67% under an adversarial disturbance for one task.

Significance. If the reported mechanism is validated, FTF would be a meaningful step toward scalable collection of force-sensitive manipulation skills from natural human interaction, avoiding robot teleoperation. The paper has strengths: it reports per-trial counts rather than only averages, states explicit assumptions and limitations, and points to code and videos. The core idea of decoupling learned force prediction from low-level force tracking is appealing and worth pursuing. However, the cross-embodiment force calibration and the operation of the force-tracking loop are insufficiently specified, and some experimental tables are internally inconsistent, so the central claim is not yet established as presented.

major comments (4)
  1. [Section 3.1, Figure 6, Eq. (3)] The force setpoint F_hat is derived from the human glove while the feedback F_t comes from the robot gripper sensor, but the manuscript does not establish that these quantities are commensurable. Figure 6 calibrates a bare AnySkin sensor against a weighing scale, not the sensor embedded in the glove fabric or mounted behind the robot's silicone cap and 3D-printed fingertip; the load path, contact area, and compliance differ substantially between these embodiments. The glove also senses only the thumb while the robot senses a single jaw, so even the contact geometry is not matched. Please provide a paired validation: apply the same physical loads through the actual glove and gripper interfaces and compare the resulting force readings. Without such evidence, the PD controller in Eq. (3) may be tracking a systematically wrong setpoint and the reported 77% success rate may not transfer to other instances of the hardware.
  2. [Section 3.4, Algorithm 1, Figure 6] The units of F_hat and F_t are not specified, and this ambiguity makes the reported convergence threshold epsilon=5 either vacuous or misleading. The calibration in Figure 6 maps sensor norms in roughly the 0-250 range to forces of 0-5 N. If F_hat and F_t are in Newtons, then the condition ||F_hat - F_t|| < 5 will almost always be satisfied immediately, so the 'force feedback' loop would perform essentially one iteration. If F_hat and F_t are raw sensor norms, then the Newton calibration in Figure 6 is not actually used in the control loop and the paper's language about tracking forces in Newtons is unsupported. Please specify the units explicitly, correct the threshold if it is a typo, and show measured convergence traces for representative rollouts to demonstrate that the loop actively adjusts the gripper.
  3. [Tables 1 and 2] The FTF column in Table 2 reports 5/15, 4/15, 0/15, 3/15, and 9/15 across the five tasks, while Table 1 reports 13/15, 9/15, 13/15, 10/15, and 13/15 for the same task names. The caption of Table 2 says 'Robot Teleop Demo,' but the text describes Table 2 as comparing FTF with baselines trained on robot teleoperation data. If the FTF numbers in Table 2 are the same human-trained policy evaluated under different conditions, that needs to be stated and reconciled; if they are a different model (for example, FTF adapted to teleoperation data), the notation is misleading. As written, the inconsistency prevents a reader from verifying the claim that FTF generally outperforms teleoperation-based baselines.
  4. [Section 4, Baselines] The baseline list names five methods: Tactile Point Policy, Continuous-Gripper Tactile Point Policy, FTF + Tactile P3-PO, Tactile P3-PO, and Continuous-Gripper Tactile P3-PO. However, Tables 1 and 2 report results only for FTF, Binary Gripper, and Continuous Gripper. No numerical results are given for the three P3-PO variants, even though the text states that 'FTF generally outperforms teleoperation baselines' and that 'FTF robustly solves all 5 force-sensitive tasks evaluated ... where baselines fail.' Please report the results for all listed baselines or explicitly restrict the claims to the baselines that were actually evaluated.
minor comments (6)
  1. [Section 3.4, Eq. (3)] The controller is described as a PD controller, but Eq. (3) contains only a proportional term in (F_hat - F_t) with gain k. Either add an explicit derivative term, name the method a proportional controller, or clarify where the derivative action enters.
  2. [Section 4, Figure 5] The text says 'As shown in Table 5' when referring to the adversarial disturbance results, but the corresponding result is presented in Figure 5 and has the visual form of a small table. Please unify the cross-reference and caption to avoid confusion.
  3. [Table 3 caption] The caption says 'masked vs umasked force tracks'; 'umasked' should be 'unmasked.' The heading of the right column ('Unmasked Force') is also ambiguous—clarify whether it means the force is provided to the policy or hidden.
  4. [Section 3.3] The policy predicts robot point tracks, gripper state, and force values, but the text states that the training loss is applied only to the robot point tracks. Please clarify how the gripper and force prediction heads are trained; if they are supervised by a separate loss term, state it explicitly.
  5. [Tables 1-3] Per-task results are based on 15 rollouts with no confidence intervals, and the 67% disturbance result is based on 10/15. Please report binomial 95% confidence intervals or otherwise quantify the uncertainty so the reader can judge the strength of the 77% aggregate claim.
  6. [Introduction and Conclusion] The text contains a duplicated word in the Conclusion ('and and remains robust under perturbations') and the introduction contains a similar typo in the list of contributions. A careful proofread would remove these.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the force-tracking mechanism is an empirical cross-embodiment assumption, not a definitional reduction, and the main success claims are judged by independent task-completion metrics.

full rationale

The paper's central derivation is not circular in the sense of the enumerated patterns. The policy is trained on human demonstrations to predict robot point tracks, gripper state, and force setpoints; success is then measured by task completion on a Franka Panda, which is external to the training objective. The PD controller in Eq. 3 tracks the predicted force, but matching the predicted force is not the evaluation metric, so the reported 77% success rate is not forced by construction. The 'one-to-one correspondence' between the glove sensor and the robot gripper sensor is asserted from using the same AnySkin sensor hardware, but this is an empirical calibration/embodiment assumption, not a logical reduction of the predicted force to the measured force. The paper cites several prior works from the same research group (AnySkin, Point Policy, BAKU, P3-PO, OpenTeach), but these are used as off-the-shelf components and are evaluated against external baselines in this paper; they do not constitute a load-bearing self-citation chain that determines the main result. The most notable weakness is the lack of paired validation that glove-side and gripper-side force readings correspond under the same physical load, and the statement that the training loss is only applied to robot point tracks is an internal inconsistency worth investigating, but neither of these is a circularity of the kind that would require a nonzero score here.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim depends on two hand-tuned controller constants and several domain assumptions about calibration and sensor comparability. These are reasonable for a real-world robotics paper, but they are exactly what must hold for the 77% result to reproduce.

free parameters (4)
  • PD controller gain k = 0.001
    Hand-chosen in Section 3.4 and stated to 'work well across all tasks'; the force-tracking loop depends on this gain.
  • Force convergence threshold epsilon = 5
    Hand-chosen in Section 3.4 as the convergence criterion for the PD loop; affects how closely the robot tracks predicted force before proceeding.
  • Gripper closure threshold = 7 cm
    Used in Section 3.2.1 to binarize the gripper state from fingertip distance; affects the policy input representation.
  • Force aggregation window = 200 fps to ~30 fps
    Aggregating glove force readings over time in Section 3.1; a design choice that could affect force fidelity.
assumptions (5)
  • domain assumption The scene is calibrated with known intrinsic and extrinsic camera matrices.
    Stated in Section 3 Assumptions; required for 3D point triangulation of hand and object keypoints.
  • domain assumption The human hand pose in the first demonstration frame equals the robot gripper pose at reset.
    Stated in Section 3 Assumptions; required for cross-embodiment trajectory retargeting.
  • domain assumption Force readings from the human glove and the robot gripper AnySkin sensor are comparable after sensor-norm-to-Newton mapping.
    Assumed in Sections 3.1 and 3.4; the PD controller uses the same predicted force values F_hat as setpoints on both platforms.
  • domain assumption Mediapipe, DIFT, and Co-Tracker provide accurate and temporally consistent 3D keypoints.
    Used in Sections 3.2.1 and 3.2.2 without in-domain validation; errors propagate into the robot trajectory and force predictions.
  • standard math Rigid transform and PD control theory are valid for the intended purposes.
    Equation (1) uses rigid transforms; Equation (3) is a standard proportional controller, assumed to converge under the chosen gain.

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Cite this review

Pith. "Pith review of Feel the Force: Contact-Driven Learning from Humans." pith.science (2026). https://pith.science/paper/FYT3TH5Z

@misc{pith2026250601944,
  author       = {Pith},
  title        = {Pith review of: Feel the Force: Contact-Driven Learning from Humans},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FYT3TH5Z}},
  note         = {Machine review of arXiv:2506.01944}
}
read the original abstract

Controlling fine-grained forces during manipulation remains a core challenge in robotics. While robot policies learned from robot-collected data or simulation show promise, they struggle to generalize across the diverse range of real-world interactions. Learning directly from humans offers a scalable solution, enabling demonstrators to perform skills in their natural embodiment and in everyday environments. However, visual demonstrations alone lack the information needed to infer precise contact forces. We present FeelTheForce (FTF): a robot learning system that models human tactile behavior to learn force-sensitive manipulation. Using a tactile glove to measure contact forces and a vision-based model to estimate hand pose, we train a closed-loop policy that continuously predicts the forces needed for manipulation. This policy is re-targeted to a Franka Panda robot with tactile gripper sensors using shared visual and action representations. At execution, a PD controller modulates gripper closure to track predicted forces-enabling precise, force-aware control. Our approach grounds robust low-level force control in scalable human supervision, achieving a 77% success rate across 5 force-sensitive manipulation tasks. Code and videos are available at https://feel-the-force-ftf.github.io.

Figures

Figures reproduced from arXiv: 2506.01944 by the authors.

Figure 1
Figure 1. FTF allows zero-shot transfer of tactile human demonstrations to a Franka Robot. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. FTF allows zero-shot transfer of tactile human demonstrations to a Franka Robot. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Hardware setup for human demonstration and robot replication using AnySkin [ [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Manipulation tasks evaluated with FTF (2) How does FTF compare to tactile policy learning methods that learn from robot teleoperation data? (3) Is FTF robust to test-time disturbances? Experimental Setup We evaluate FTF on a Franka Panda robot, operating in a real-worl…
Figure 5
Figure 5. Figure 5: Analysis of FTF with test time adversarial disturbance Task FTF Place bag of chips on plate 10/15 FTF is robust to test-time disturbances. In the placing a bag of chips on a plate task, we introduce disturbances by physically interacting with the bag by holding it down…
Figure 6
Figure 6. Figure 6: (Left) Mapping between sensor norm and applied force across various contact modes. [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: Comparison of gripper action space across human (left) and robot (right) baselines under [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Visual comparison of tactile human demonstrations (left) and force-sensitive robot manip [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]

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Reviewed August 7, 2026 · model on record in the stance chip above.