Beyond Reconstruction: What Matters in Action Tokenization for Robot Policies?
Overview
ProAct, a new action tokenizer method, improves robot policy performance with 11.3% higher rollout success across benchmarks. It is policy-agnostic and uses action datasets for training, enhancing predictability and robustness in vision-language-action policies and manipulation tasks.
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- arXiv Robotics — research abstractsBeyond Reconstruction: What Matters in Action Tokenization for Robot Policies?
This research introduces ProAct, a novel action tokenizer training method that improves downstream predictability and robustness by augmenting reconstruction objectives. Evaluated across multiple benchmarks and real-world tasks, ProAct achieves average gains of 11.3 percentage points in rollout success and 21.8-36.7 percentage points in vision-language-action policies and robotic manipulation.
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