Robot action refinement improves manipulation success with less delay
Refine Actions If Needed: Adaptive Action Chunk Refinement for Robot Manipulation
Robotics
Summary
Robot programs that quickly decide what to do often make small mistakes that reduce success. Slower, more thorough methods get better results but take more time. The paper introduces RAIN, a system that predicts when and how much to improve quick robot actions before running slower checks, saving time while staying accurate. Tests in simulated and real settings show RAIN improves robot task success with less waiting for decisions.
What this means in practice
- •For robotics engineers: Improve robot arm task success rates by dynamically refining action decisions only when needed, reducing planning delays.
- •For automation system designers: Design faster robotic manipulation systems that balance computation and error correction by predicting necessary refinement levels.
Authors
Hanlin Liu, Zichen Zhou, Jiacheng Zhang, Liuyuan Na, Jin Jin, Guangyan Chen, Yufeng Yue, Gen Li, Fangqiang Ding
Abstract
Computationally lightweight manipulation policies predict executable actions with low inference latency, but prediction errors can limit task success. Iterative generative policies can achieve higher task success rates, but repeated network evaluations increase inference latency. Refining action proposals from computationally lightweight policies can combine these strengths, but the amount of refinement needed varies across states. We introduce RAIN (Refine Actions If Needed), a framework that predicts action errors at different refinement levels to decide whether and how much to refine each executable proposal, before performing any refinement. We train an ensemble to predict these errors by comparing action proposals and their refined versions with expert actions from offline demonstrations, without requiring explicit labels of refinement needs. Calibration balances uncertainty, computation, and temporal consistency. We evaluate RAIN on eight ManiSkill tasks and four real-robot tasks. In simulation, our policy achieves 66.6% success, approaching the best evaluated diffusion-policy success rate with 5.71 times lower policy latency. On the real robot, success increases from 55.0% with Action Chunking with Transformers (ACT) to 72.5%, with comparable average successful completion time. Matched controls show that dynamic routing maintains the strongest observed fixed-refinement success level with 26.6% fewer refiner evaluations. Project page: https://rain-policy.github.io/.