Tactile-Morph Skills: Energy-Based Control Meets Data-Driven Learning

TL;DR

This paper presents a novel tactile-morph skill framework that combines energy-based control with data-driven learning, enabling safe, adaptable, and transferable robotic manipulation skills for industrial tasks.

Contribution

It introduces a unified control and learning system that estimates energy levels for safe and effective manipulation, facilitating zero-shot skill transfer across different tasks and surfaces.

Findings

Improved accuracy in manipulation tasks.

Zero-shot transfer of skills to new surfaces.

Enhanced safety through energy-based stopping criteria.

Abstract

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.