FoAR: Force-Aware Reactive Policy for Contact-Rich Robotic Manipulation (2411.15753v1)

Abstract: 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/

Force-Aware Reactive Policy for Contact-Rich Robotic Manipulation

The paper "FoAR: Force-Aware Reactive Policy for Contact-Rich Robotic Manipulation" presents a comprehensive approach to enhancing robotic manipulation in environments characterized by contact-rich tasks. These tasks, which require sustained and intricate contact with objects or environments, pose considerable challenges to traditional vision-based manipulation policies typically deployed in robotic systems. These limitations are particularly relevant when dealing with tasks such as assembly, wiping, and peeling, which demand nuanced interactions and real-time adaptability due to the complex dynamics of contact.

The authors introduce FoAR, a multi-modal policy that incorporates high-frequency force/torque sensing fused with visual inputs to improve manipulation performance in contact-rich environments. The core innovation lies in the policy's utilization of a future contact predictor, which dynamically manages the contribution of force/torque data during different manipulation phases. This fusion mechanism facilitates the transition between non-contact and contact conditions, thereby enhancing precision and control via simple position-based strategies while avoiding reliance on complex control parameters like stiffness.

Methodological Overview

The proposed methodology builds on RISE, a state-of-the-art robot imitation policy, by integrating additional force/torque data to address the inherent shortcomings of vision-only systems. This approach involves several key components:

1. Point Cloud and Force/Torque Encoding: The policy leverages a point cloud encoder to process environmental observations with a sparse ResNet architecture for visual perception, while force/torque data is encoded via a transformer to extract relevant features for decision-making.
2. Future Contact Prediction: A specialized module estimates the probability of future contact states based on current visual and force/torque inputs. This predictor informs the multimodal fusion process, allowing for context-sensitive integration of force data.
3. Reactive Control Framework: The reactive control strategy leverages the predicted contact probability to dynamically adjust applied forces during task execution. This real-time adjustment improves task performance under varied and unexpected environmental disturbances.

Experimental Evaluation

Empirical evaluations demonstrate the efficacy of FoAR across several challenging tasks, including wiping, peeling, and chopping, which demand robust force control and adaptability. Notably, the policy exhibits superior performance in both contact phases (e.g., tool use) and non-contact transitional phases, outperforming baseline methods.

• Quantitative Results: The introduction of force/torque sensing results in significant improvements in manipulation scores and action success rates across all tasks compared to purely vision-based baselines. For instance, the FoAR policy achieves perfect action success rates in wiping tasks and displays superior consistency in peeling operations.
• Robustness Experiments: FoAR exhibits consistent performance under dynamic disturbances, such as object repositioning or modified contact surfaces, affirming the robustness and adaptability of its control strategy.

Implications and Future Directions

The paper's insights extend practical implications for developing more dexterous and autonomous robotic systems capable of handling real-world tasks requiring precision and force feedback. The authors suggest potential future advancements, including the integration of more sophisticated control strategies like compliance control and exploring applications in multi-robot or humanoid setups for complex interaction environments.

Overall, the FoAR policy contributes a significant advancement in contact-rich robotic manipulation, underscoring the importance of multimodal integration for future developments in robotics and artificial intelligence. As AI continues to evolve, leveraging diverse sensory information will be instrumental in achieving higher levels of autonomy and real-world functionality.