触覚・形態スキル：エネルギーベース制御とデータ駆動学習の融合
Tactile-Morph Skills: Energy-Based Control Meets Data-Driven Learning
力とインピーダンスの統合制御にデータ駆動学習を組み合わせ、エネルギー推定で安全性を確保しつつ未知の形状や表面へスキルをゼロショット転移できる枠組みを提案した。
著者: Anran Zhang, Kübra Karacan, Hamid Sadeghian, Yansong Wu, Fan Wu, Sami Haddadin
分類: cs.RO
原文アブストラクト
Robotic manipulation is essential for modernizing factories and automating industrial tasks like polishing, which require advanced tactile abilities. These robots must be easily set up, safely work with humans, learn tasks autonomously, and transfer skills to similar tasks. Addressing these needs, we introduce the tactile-morph skill framework, which integrates unified force-impedance control with data-driven learning. Our system adjusts robot movements and force application based on estimated energy levels for the desired trajectory and force profile, ensuring safety by stopping if energy allocated for the control runs out. Using a Temporal Convolutional Network, we estimate the energy distribution for a given motion and force profile, enabling skill transfer across different tasks and surfaces. Our approach maintains stability and performance even on unfamiliar geometries with similar friction characteristics, demonstrating improved accuracy, zero-shot transferable performance, and enhanced safety in real-world scenarios. This framework promises to enhance robotic capabilities in industrial settings, making intelligent robots more accessible and valuable.
