実世界からシミュレーション、そして実世界へ：共有自律による効率的で信頼性の高い遠隔操作
Efficient and Reliable Teleoperation through Real-to-Sim-to-Real Shared Autonomy
実世界の遠隔操作データから学習した人間の代理モデルを用いて、シミュレーションで残差コパイロットポリシーを訓練し、実世界の微細な操作タスクで人間の遠隔操作を支援する共有自律フレームワークを提案。
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2. 先行研究と比べてどこがすごい？
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著者: Shuo Sha, Yixuan Wang, Binghao Huang, Antonio Loquercio, Yunzhu Li
分類: cs.RO
原文アブストラクト
Fine-grained, contact-rich teleoperation remains slow, error-prone, and unreliable in real-world manipulation tasks, even for experienced operators. Shared autonomy offers a promising way to improve performance by combining human intent with automated assistance, but learning effective assistance in simulation requires a faithful model of human behavior, which is difficult to obtain in practice. We propose a real-to-sim-to-real shared autonomy framework that augments human teleoperation with learned corrective behaviors, using a simple yet effective k-nearest-neighbor (kNN) human surrogate to model operator actions in simulation. The surrogate is fit from less than five minutes of real-world teleoperation data and enables stable training of a residual copilot policy with model-free reinforcement learning. The resulting copilot is deployed to assist human operators in real-world fine-grained manipulation tasks. Through simulation experiments and a user study with sixteen participants on industry-relevant tasks, including nut threading, gear meshing, and peg insertion, we show that our system improves task success for novice operators and execution efficiency for experienced operators compared to direct teleoperation and shared-autonomy baselines that rely on expert priors or behavioral-cloning pilots. In addition, copilot-assisted teleoperation produces higher-quality demonstrations for downstream imitation learning.
