RoboPace: Contact-Aware Time-Optimal Retiming for Action-Chunk Policies

2026-10-07

AI Summary

This study addresses the challenge that directly inheriting human demonstration temporal profiles in robot learning often leads to an imbalance between contact safety and execution efficiency. To this end, it proposes a training-free online retiming layer that eliminates the need for policy retraining. By preserving geometric paths while integrating contact prediction with action chunking, the method dynamically adjusts execution speed based on predicted contact states, thereby unifying contact-dependent velocity limits with kinodynamic constraints. Experimental evaluations on a dual-arm robotic platform demonstrate that, compared to a conservative slow-execution baseline, the proposed approach reduces task completion time by half while significantly improving success rates and maintaining high reliability.

Abstract

Robot manipulation data collection has been shifting from teleoperation toward robot-free demonstrations, through interfaces such as the Universal Manipulation Interface (UMI) or directly from human hands. Vision-Language-Action (VLA) policies trained on such data inherit the demonstrator's timing. Yet human timing does not directly transfer to robots: compliant hands tolerate fast contact, whereas robots may overshoot due to actuator and tracking limitations; conversely, robots can move faster in free space. This motivates a unified approach that reconciles execution speed with contact safety. We present RoboPace, an online retiming layer that preserves the policy's geometric path while adapting its timing, respecting the target robot's kinematic and dynamic constraints. It adapts execution speed based on predicted contact, jointly accounting for contact-dependent speed limits and the robot's motion constraints. The method requires no policy retraining and operates in real time. Across three contact-rich tasks on a dual-arm robot, faster uniform execution and physical-limit-only retiming largely fail. RoboPace instead achieves higher overall success than slow uniform execution while completing four of five commands in approximately half the time, retaining the reliability of slow execution without its time cost.

Problem

• Robot Manipulation
• Action-Chunk Policies
• Contact-Aware Execution
• Time-Optimal Retiming
• Vision-Language-Action

Innovation

• Contact-Aware Retiming
• Action-Chunk Policies
• Time-Optimal Execution
• Vision-Language-Action (VLA)
• Kinodynamic Constraints