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FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learning
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FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learning
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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/
Forward citations
Cited by 19 Pith papers
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CoRAL: Contact-Rich Adaptive LLM-based Control for Robotic Manipulation
CoRAL lets LLMs act as adaptive cost designers for motion planners while using VLM priors and online identification to handle unknown physics, achieving over 50% higher success rates than baselines in unseen contact-r...
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FA-RDP: A Frequency-Adaptive Reactive Diffusion Policy for Contact-Rich Manipulation
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.
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Never Too Late for Force: Accelerating VLA Post-Training with Reactive Force Injection
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.
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OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies
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.
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Scalable Behavior Cloning with Open Data, Training, and Evaluation
Releases the largest open teleoperation dataset for robot manipulation together with hardware, simulation, and training infrastructure to support scalable behavior cloning.
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Flow-based Policy Adaptation without Policy Updates
GLOVES learns flow models from limited expert demonstrations to selectively correct actions from non-expert policies or operators toward expert distributions using reverse-flow OOD detection as an intervention gate.
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Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning
Incremental Iterative Reference Learning Control refines accelerated demonstrations to achieve up to 10x faster execution in contact-rich imitation learning with 22.5% better trajectory similarity than direct IRLC and...
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SCFields fuses semantics and contact data in a sim-to-real pipeline to enable category-level generalization for tactile tool manipulation with diffusion policies.
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ESPADA uses semantic segmentation from VLMs and LLMs plus DTW to downsample non-critical segments in demonstrations, delivering about 2x faster robot execution in behavior cloning while maintaining task success rates.
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Input-gated Bilateral Teleoperation: An Easy-to-implement Force Feedback Teleoperation Method for Low-cost Hardware
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.
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Seeing Touch from Motion: A Unified Modality-Aware Visuo-Tactile Policy with Tactile Motion Correlation
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IMPACT: Learning Internal-Model Predictive Control for Forceful Robotic Manipulation
IMPACT decouples forceful manipulation into task-planning and internal-model predictive control, claiming higher success rates, better generalization to unseen weights, and improved safety and energy efficiency in sim...
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CoRAL: Contact-Rich Adaptive LLM-based Control for Robotic Manipulation
CoRAL lets LLMs design objective functions for robot motion planners and uses vision-language models plus real-time identification to adapt to unknown physical properties, raising success rates by over 50 percent on n...
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ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning
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.
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Flow with the Force Field: Learning 3D Compliant Flow Matching Policies from Force and Demonstration-Guided Simulation Data
Framework generates force-informed sim data from one demo to train compliant visuomotor flow matching policies, showing reliable contact on real-robot block flipping and bi-manual tasks.
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