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FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learning

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arxiv 2502.17432 v2 pith:UYBUTX6B submitted 2025-02-24 cs.RO cs.AIcs.CVcs.LG

Jason Jingzhou Liu , Yulong Li , Kenneth Shaw , Tony Tao , Ruslan Salakhutdinov , Deepak Pathak This is my paper · ORCID
classification cs.ROcs.AIcs.CVcs.LG
keywords policycurriculumforcecontact-richfactrlearningtasksinformation
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Many contact-rich tasks humans perform, such as box pickup or rolling dough, rely on force feedback for reliable execution. However, this force information, which is readily available in most robot arms, is not commonly used in teleoperation and policy learning. Consequently, robot behavior is often limited to quasi-static kinematic tasks that do not require intricate force-feedback. In this paper, we first present a low-cost, intuitive, bilateral teleoperation setup that relays external forces of the follower arm back to the teacher arm, facilitating data collection for complex, contact-rich tasks. We then introduce FACTR, a policy learning method that employs a curriculum which corrupts the visual input with decreasing intensity throughout training. The curriculum prevents our transformer-based policy from over-fitting to the visual input and guides the policy to properly attend to the force modality. We demonstrate that by fully utilizing the force information, our method significantly improves generalization to unseen objects by 43\% compared to baseline approaches without a curriculum. Video results, codebases, and instructions at https://jasonjzliu.com/factr/

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Cited by 10 Pith papers

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

  1. Demystifying When and Why VLAs Fail in Contact-Rich Tasks and How to Fix Them

    cs.RO 2026-08 conditional novelty 6.0 of 10

    A noise-schedule change and time-aware force injection lift a VLA policy's average success on five contact-rich manipulation tasks from 41% to 66%.

  2. FA-RDP: A Frequency-Adaptive Reactive Diffusion Policy for Contact-Rich Manipulation

    cs.RO 2026-07 conditional novelty 6.0 of 10

    A shared visual-force diffusion policy with a multimodality indicator and manifold consistency distillation raises contact-rich task success to 81.7% while keeping diverse pre-contact modes.

  3. Never Too Late for Force: Accelerating VLA Post-Training with Reactive Force Injection

    cs.RO 2026-07 conditional novelty 6.0 of 10

    Injecting recent end-effector force into a pretrained VLA through a zero-initialized reactive action expert plus online DAgger improves contact-rich manipulation over vision-only post-training.

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

    cs.RO 2026-07 conditional novelty 6.0 of 10

    A policy-agnostic two-stage real-world RL method learns tactile residual corrections on frozen visual policies, lifting contact-rich task success from 5–40% to 85–100% in under 80 minutes.

  5. Input-gated Bilateral Teleoperation: An Easy-to-implement Force Feedback Teleoperation Method for Low-cost Hardware

    cs.RO 2025-09 conditional novelty 6.0 of 10

    A simple bilateral teleoperation law that clamps the leader's control input to the follower's input achieves both easy free motion and stable contact on low-cost hardware.

  6. TA-VLA: Elucidating the Design Space of Torque-aware Vision-Language-Action Models

    cs.RO 2025-09 conditional novelty 6.0 of 10

    Feeding torque history as a single decoder token and adding torque prediction as an auxiliary objective improves pretrained VLA success rates on contact-rich manipulation, with large gains on button pushing and charge...

  7. PAPRLE (Plug-And-Play Robotic Limb Environment): A Modular Ecosystem for Robotic Limbs

    cs.RO 2025-07 conditional novelty 6.0 of 10

    PAPRLE is a modular teleoperation ecosystem that pairs diverse input devices with arbitrary robot limb configurations and adds force feedback even when leader and follower morphologies differ.

  8. ViTacWorld: Scaling Visuo-Tactile World Models for Contact-Rich Robot Manipulation

    cs.RO 2026-07 conditional novelty 5.0 of 10

    An action-conditioned visuo-tactile world model generates synthetic camera-plus-touch rollouts that, mixed with real demonstrations, improve downstream contact-rich manipulation policies.

  9. ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning

    cs.RO 2025-12 conditional novelty 5.0 of 10

    A causal-attention diffusion policy that fuses slow vision with fast force feedback and predicts a virtual target outperforms hierarchical baselines on two contact-rich manipulation tasks.

  10. An Optimization-Augmented Control Framework for Single and Coordinated Multi-Arm Robotic Manipulation

    cs.RO 2025-06 conditional novelty 3.0 of 10

    A multi-modal controller that switches between optimization-based planning and force control completes simulated single-arm, bimanual, and four-arm manipulation tasks.

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