Beyond Implicit Force: Evaluating Explicit Force-Torque Proxies in Action Chunking with Transformers
- URL: http://arxiv.org/abs/2607.14578v1
- Date: Thu, 16 Jul 2026 05:16:05 GMT
- Title: Beyond Implicit Force: Evaluating Explicit Force-Torque Proxies in Action Chunking with Transformers
- Abstract summary: Action Chunking with Transformers (ACT) has shown strong performance in fine-grained manipulation.<n>Many deployments collect demonstrations through leader-follower teleoperation.<n>This paper examines whether ACT's apparent force-awareness depends on this hidden interaction cue.
- Score: 27.33711271960601
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Contact-rich manipulation requires policies to infer interaction state from signals that are often weakly observable through vision and kinematics alone. Action Chunking with Transformers (ACT) has shown strong performance in fine-grained manipulation, but many deployments collect demonstrations through leader-follower teleoperation, where tracking error between commanded leader motion and executed follower motion implicitly encodes contact, resistance, and constraint violation. This paper examines whether ACT's apparent force-awareness depends on this hidden interaction cue. We introduce an observation-centric ACT variant that predicts future follower joint states instead of leader commands, thereby removing the teleoperation-induced discrepancy signal while preserving the rest of the learning pipeline. We then evaluate whether simple joint-torque proxies, derived from onboard motor current or joint effort, can recover contact-aware behavior without external force/torque sensors. Across four real-world tasks spanning surface following, insertion, stiffness discrimination, and force-based stopping, removing the implicit cue leads to severe failures in force-critical phases. In contrast, torque-augmented policies recover robust contact behavior and improve the base ACT policy. These results demonstrate that, on real hardware, the implicit teleoperation cue is a recoverable source of force-awareness, where torque signals are available, a simple proxy matches, surpasses, or further enhances it.
Related papers
- Force-Aware Reinforcement Learning with Hybrid Sensorless Force Estimation for Wheeled-Legged Loco-Manipulation [14.501012360823886]
Force-controlled loco-manipulation requires a whole-body policy to coordinate locomotion and arm motion.<n>This paper presents a force-aware reinforcement learning approach with hybrid sensorless force estimation for wheeled-legged loco-manipulation.
arXiv Detail & Related papers (2026-09-12T07:46:53Z) - Current as Touch: Proprioceptive Contact Feedback for Compliant Dexterous Manipulation [21.695801136739558]
We propose a proprioception-driven framework that learns contact-aware compliance cues from motor current and joint states.<n>Our method predicts a compliance reference position: an ideal joint-position target for a standard PD controller.<n>Experiments show stable compliant grasping, safer and more efficient teleoperation, and improved downstream policy learning without external tactile or force sensors.
arXiv Detail & Related papers (2026-07-03T17:59:50Z) - Seeing Touch from Motion: A Unified Modality-Aware Visuo-Tactile Policy with Tactile Motion Correlation [74.87716678195099]
Visuo-Tactile policies leveraging optical tactile sensors have shown great promise in contact-rich manipulation.<n>Despite their advantages, extracting fine-grained contact states necessary for contact-rich manipulation remains an open challenge.<n>We propose a motion-aware tactile representation to facilitate contact-rich manipulation.
arXiv Detail & Related papers (2026-06-29T08:20:18Z) - Transferring Contact, Not Just Motion: Compliant Grasping Across Dexterous Hands [0.01268579273097071]
Dexterous grasping depends on contact regulation, not motion alone.<n>Existing cross-embodiment dexterous policies unify motion through retargeted hand poses or latent actions, but force feedback remains tied to each hand's sensing and actuation.<n>This work introduces a cross-embodiment force-position interface for contact-aware manipulation across heterogeneous dexterous hands.
arXiv Detail & Related papers (2026-06-14T00:01:36Z) - Beyond Binary: Sim-to-Real Dexterous Manipulation with Physics-Grounded Contact Representation [51.463992799813816]
A primary bottleneck in contact-rich manipulation is the difficulty of collecting real-world data.<n>We introduce Center-of-Pressure, an effective tactile representation grounded in physical principles.<n>We evaluate CoP on two blind, challenging contact-rich manipulation tasks.
arXiv Detail & Related papers (2026-05-27T17:59:02Z) - ForceFlow: Learning to Feel and Act via Contact-Driven Flow Matching [53.30290192030814]
ForceFlow is a force-aware reactive framework built upon flow matching.<n>We show that ForceFlow achieves a 37% success rate improvement over the strong baseline ForceVLA.<n>It also exhibits accurate force signal prediction and superior performance in contact force self-regulation.
arXiv Detail & Related papers (2026-05-11T13:27:00Z) - Mind the Gap: Learning Implicit Impedance in Visuomotor Policies via Intent-Execution Mismatch [7.078279704479455]
We propose a Dual-State Conditioning framework that shifts the learning objective to "Intent Cloning" (master command)<n>By predicting the master intent, our policy learns to generate a "virtual equilibrium point", effectively realizing implicit impedance control.<n>This presents a minimalist behavior cloning framework for low-cost hardware, enabling force perception and dynamic compensation without relying on explicit force sensing.
arXiv Detail & Related papers (2026-02-09T15:18:12Z) - UniForce: A Unified Latent Force Model for Robot Manipulation with Diverse Tactile Sensors [51.88112610411651]
We propose a novel unified tactile representation learning framework that learns a shared latent force space across diverse tactile sensors.<n>UniForce reduces cross-sensor domain shift by jointly modeling inverse dynamics (image-to-force) and forward dynamics (force-to-image)<n>To avoid reliance on expensive external force/torque (F/T) sensors, we exploit static equilibrium and collect force-paired data via direct sensor--object--sensor interactions.
arXiv Detail & Related papers (2026-02-01T11:03:01Z) - ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning [52.86018040861575]
We propose a unified end-to-end visual-force diffusion policy that integrates visual planning and reactive force control within a single network.<n>We introduce Structural Slow-Fast Learning, a mechanism utilizing causal attention to simultaneously process asynchronous visual and force tokens.<n>Experiments on contact-rich tasks demonstrate that ImplicitRDP significantly outperforms both vision-only and hierarchical baselines.
arXiv Detail & Related papers (2025-12-11T18:59:46Z) - Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation [58.95799126311524]
Humans can accomplish contact-rich tasks using vision and touch, with highly reactive capabilities such as fast response to external changes and adaptive control of contact forces.<n>Existing visual imitation learning approaches rely on action chunking to model complex behaviors.<n>We introduce TactAR, a low-cost teleoperation system that provides real-time tactile feedback through Augmented Reality.
arXiv Detail & Related papers (2025-03-04T18:58:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.