mlsanityData to 14 Oct 2026

FoAR: Force-Aware Reactive Policy for Contact-Rich Robotic Manipulation

arXiv 2411.15753 ↗First posted 24 Nov 2024cs.ROcited by 33

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Abstract (arXiv)

Contact-rich tasks present significant challenges for robotic manipulation policies due to the complex dynamics of contact and the need for precise control. Vision-based policies often struggle with the skill required for such tasks, as they typically lack critical contact feedback modalities like force/torque information. To address this issue, we propose FoAR, a force-aware reactive policy that combines high-frequency force/torque sensing with visual inputs to enhance the performance in contact-rich manipulation. Built upon the RISE policy, FoAR incorporates a multimodal feature fusion mechanism guided by a future contact predictor, enabling dynamic adjustment of force/torque data usage between non-contact and contact phases. Its reactive control strategy also allows FoAR to accomplish contact-rich tasks accurately through simple position control. Experimental results demonstrate that FoAR significantly outperforms all baselines across various challenging contact-rich tasks while maintaining robust performance under unexpected dynamic disturbances. Project website: https://tonyfang.net/FoAR/

Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Nov 2024.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 8; 2026: 25.

33 papers.

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  1. 2025Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulationcited by 108
  2. 2025ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulationcited by 91
  3. 2025VLA-Touch: Enhancing Vision-Language-Action Models with Dual-Level Tactile Feedbackcited by 38
  4. 2025FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learningcited by 34
  5. 2025TA-VLA: Elucidating the Design Space of Torque-aware Vision-Language-Action Modelscited by 32
  6. 2025AirExo-2: Scaling up Generalizable Robotic Imitation Learning with Low-Cost Exoskeletonscited by 18
  7. 2026FD-VLA: Force-Distilled Vision-Language-Action Model for Contact-Rich Manipulationcited by 17
  8. 2026FAVLA: A Force-Adaptive Fast-Slow VLA model for Contact-Rich Robotic Manipulationcited by 15
  9. 2026AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Modelscited by 9
  10. 2026Towards Human-Like Manipulation through RL-Augmented Teleoperation and Mixture-of-Dexterous-Experts VLAcited by 5
  11. 2026ForceFlow: Learning to Feel and Act via Contact-Driven Flow Matchingcited by 5
  12. 2025Flow with the Force Field: Learning 3D Compliant Flow Matching Policies from Force and Demonstration-Guided Simulation Datacited by 3
21 more
  1. 2026FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learningcited by 2
  2. 2026Seeing Touch from Motion: A Unified Modality-Aware Visuo-Tactile Policy with Tactile Motion Correlationcited by 2
  3. 2026SoftVTBench: A Safety-Aware Visuo-Tactile Benchmark for Physically Constrained Robotic Manipulation of Deformable Objects (Early Version)cited by 2
  4. 2026Demystifying When and Why VLAs Fail in Contact-Rich Tasks and How to Fix Themcited by 2
  5. 2026TacForcing: Streaming Action Generation with Execution-Time Tactile Feedbackcited by 2
  6. 2026Facet-0: A Robotic Foundation Model for Contact-Rich Precise Manipulationcited by 2
  7. 2026TacPAC: Tactile Prediction and Real-Time Action Correction in World-Action Models for Contact-Rich Manipulationcited by 2
  8. 2025AnoF-Diff: One-Step Diffusion-Based Anomaly Detection for Forceful Tool Usecited by 1
  9. 2026Selective Perception for Robot: Task-Aware Attention in Multimodal VLAcited by 1
  10. 2026How to Peel with a Knife: Aligning Fine-Grained Manipulation with Human Preferencecited by 1
  11. 2026KineVLA: Towards Kinematics-Aware Vision-Language-Action Models with Bi-Level Action Decompositioncited by 1
  12. 2026Asynchronous Multimodal Diffusion Policy Composition via Latency-Aware Guidance Fusioncited by 1
  13. 2026VIDP: Variable Impedance Diffusion Policy for Compliant Robot Manipulation from Diverse Demonstrationscited by 1
  14. 2026CR-VLA-Force: Learning Control-aware Compliance VLA Model for Robust Contact-rich Robotic Manipulationcited by 1
  15. 2026RAPID: Reconfigurable, Adaptive Platform for Iterative Designcited by 0
  16. 2026A Hierarchical Approach to Imitation Learning for Manipulation Tasks Requiring Time Varying Forcescited by 0
  17. 2026DeCAL: Towards Physically-Grounded Dexterous Vision-Language-Action Models via Contact-Aware Latent Co-Imaginationcited by 0
  18. 2026Dissecting Motion-Prior Regularization for Data-Scarce Robotic Insertioncited by 0
  19. 2026TAO-Force: Unifying Force-Aware Perception and Fast-Slow Control for Contact-Rich Manipulationcited by 0
  20. 2026ForceRFT: Refining VLA Actions through Force-Guided Residual Reinforcement Learningcited by 0
  21. 2026ROMA: LLM System for Real-World Object-Centric Multi-Sensory Active Perceptioncited by 0