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

arXiv: https://arxiv.org/abs/2411.15753 · HTML: https://mlsanity.com/paper/2411.15753/

## Key facts

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

## Built on by (most cited first, 30 of 33)

- [Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation](https://mlsanity.com/paper/2503.02881/) (2025)
- [ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation](https://mlsanity.com/paper/2505.22159/) (2025)
- [VLA-Touch: Enhancing Vision-Language-Action Models with Dual-Level Tactile Feedback](https://mlsanity.com/paper/2507.17294/) (2025)
- [FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learning](https://mlsanity.com/paper/2502.17432/) (2025)
- [TA-VLA: Elucidating the Design Space of Torque-aware Vision-Language-Action Models](https://mlsanity.com/paper/2509.07962/) (2025)
- [AirExo-2: Scaling up Generalizable Robotic Imitation Learning with Low-Cost Exoskeletons](https://mlsanity.com/paper/2503.03081/) (2025)
- [FD-VLA: Force-Distilled Vision-Language-Action Model for Contact-Rich Manipulation](https://mlsanity.com/paper/2602.02142/) (2026)
- [FAVLA: A Force-Adaptive Fast-Slow VLA model for Contact-Rich Robotic Manipulation](https://mlsanity.com/paper/2602.23648/) (2026)
- [AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models](https://mlsanity.com/paper/2605.07308/) (2026)
- [Towards Human-Like Manipulation through RL-Augmented Teleoperation and Mixture-of-Dexterous-Experts VLA](https://mlsanity.com/paper/2603.08122/) (2026)
- [ForceFlow: Learning to Feel and Act via Contact-Driven Flow Matching](https://mlsanity.com/paper/2605.11048/) (2026)
- [Flow with the Force Field: Learning 3D Compliant Flow Matching Policies from Force and Demonstration-Guided Simulation Data](https://mlsanity.com/paper/2510.02738/) (2025)
- [FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning](https://mlsanity.com/paper/2606.12406/) (2026)
- [Seeing Touch from Motion: A Unified Modality-Aware Visuo-Tactile Policy with Tactile Motion Correlation](https://mlsanity.com/paper/2606.29941/) (2026)
- [SoftVTBench: A Safety-Aware Visuo-Tactile Benchmark for Physically Constrained Robotic Manipulation of Deformable Objects (Early Version)](https://mlsanity.com/paper/2607.04234/) (2026)
- [Demystifying When and Why VLAs Fail in Contact-Rich Tasks and How to Fix Them](https://mlsanity.com/paper/2608.01402/) (2026)
- [TacForcing: Streaming Action Generation with Execution-Time Tactile Feedback](https://mlsanity.com/paper/2608.25798/) (2026)
- [Facet-0: A Robotic Foundation Model for Contact-Rich Precise Manipulation](https://mlsanity.com/paper/2609.01596/) (2026)
- [TacPAC: Tactile Prediction and Real-Time Action Correction in World-Action Models for Contact-Rich Manipulation](https://mlsanity.com/paper/2609.05266/) (2026)
- [AnoF-Diff: One-Step Diffusion-Based Anomaly Detection for Forceful Tool Use](https://mlsanity.com/paper/2509.15153/) (2025)
- [Selective Perception for Robot: Task-Aware Attention in Multimodal VLA](https://mlsanity.com/paper/2602.15543/) (2026)
- [How to Peel with a Knife: Aligning Fine-Grained Manipulation with Human Preference](https://mlsanity.com/paper/2603.03280/) (2026)
- [KineVLA: Towards Kinematics-Aware Vision-Language-Action Models with Bi-Level Action Decomposition](https://mlsanity.com/paper/2603.17524/) (2026)
- [Asynchronous Multimodal Diffusion Policy Composition via Latency-Aware Guidance Fusion](https://mlsanity.com/paper/2607.17257/) (2026)
- [VIDP: Variable Impedance Diffusion Policy for Compliant Robot Manipulation from Diverse Demonstrations](https://mlsanity.com/paper/2608.06210/) (2026)
- [CR-VLA-Force: Learning Control-aware Compliance VLA Model for Robust Contact-rich Robotic Manipulation](https://mlsanity.com/paper/2609.05832/) (2026)
- [RAPID: Reconfigurable, Adaptive Platform for Iterative Design](https://mlsanity.com/paper/2602.06653/) (2026)
- [A Hierarchical Approach to Imitation Learning for Manipulation Tasks Requiring Time Varying Forces](https://mlsanity.com/paper/2608.03103/) (2026)
- [DeCAL: Towards Physically-Grounded Dexterous Vision-Language-Action Models via Contact-Aware Latent Co-Imagination](https://mlsanity.com/paper/2609.09119/) (2026)
- [Dissecting Motion-Prior Regularization for Data-Scarce Robotic Insertion](https://mlsanity.com/paper/2609.17484/) (2026)

## 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/