ReSteer: マルチタスクロボットポリシーの操縦可能性の定量化と改善
ReSteer: Quantifying and Refining the Steerability of Multitask Robot Policies
マルチタスクロボットポリシーが指示に従ってタスクを切り替える能力（操縦可能性）を定量化し、データ生成と自己改善により向上させるフレームワークを提案した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Zhenyang Chen, Alan Tian, Liquan Wang, Benjamin Joffe, Yingyan Celine Lin, Yuxiao Chen, Siddharth Karamcheti, Danfei Xu
分類: cs.RO
原文アブストラクト
Despite strong multi-task pretraining, existing policies often exhibit poor task steerability. For example, a robot may fail to respond to a new instruction ``put the bowl in the sink" when moving towards the oven, executing ``close the oven", even though it can complete both tasks when executed separately. We propose ReSteer, a framework to quantify and improve task steerability in multitask robot policies. We conduct an exhaustive evaluation of state-of-the-art policies, revealing a common lack of steerability. We find that steerability is associated with limited overlap among training task trajectory distributions, and introduce a proxy metric to measure this overlap from policy behavior. Building on this insight, ReSteer improves steerability via three components: (i) a steerability estimator that identifies low-steerability states without full-rollout evaluation, (ii) a steerable data generator that synthesizes motion segments from these states, and (iii) a self-refinement pipeline that improves policy steerability using the generated data. In simulation on LIBERO, ReSteer improves steerability by 11\% over 18k rollouts. In real-world experiments, we show that improved steerability is critical for interactive use, enabling users to instruct robots to perform any task at any time. We hope this work motivates further study on quantifying steerability and data collection strategies for large robot policies.
関連論文
- 低ランク行列によるマルチタスク学習の競合解消マルチタスク学習
- 不均衡データに対するロボット知覚のマルチタスク学習マルチタスク学習
- 学習進捗とエネルギー消費に基づくタスク交互マルチタスク学習マルチタスク学習
- DenseMTL: 密なマルチタスク学習のためのクロスタスク注意機構マルチタスク学習
- マルチタスク学習における転移の測定と活用マルチタスク学習
- 自動運転向けマルチタスクネットワークの動的タスク重み付け手法マルチタスク学習
