Optimizing Demonstrated Robot Manipulation Skills for Temporal Logic Constraints

TL;DR

This paper introduces a method that integrates formal temporal logic specifications into learning-from-demonstration for robotic manipulation, enabling robots to better adhere to timing constraints and complex task requirements.

Contribution

The authors propose a novel approach combining Signal Temporal Logic with black-box optimization to enhance LfD skills for complex, timed manipulation tasks.

Findings

Effective in simulation for complex temporal tasks

Successful real-world industrial demonstrations

Improves task adherence beyond traditional LfD methods

Abstract

For performing robotic manipulation tasks, the core problem is determining suitable trajectories that fulfill the task requirements. Various approaches to compute such trajectories exist, being learning and optimization the main driving techniques. Our work builds on the learning-from-demonstration (LfD) paradigm, where an expert demonstrates motions, and the robot learns to imitate them. However, expert demonstrations are not sufficient to capture all sorts of task specifications, such as the timing to grasp an object. In this paper, we propose a new method that considers formal task specifications within LfD skills. Precisely, we leverage Signal Temporal Logic (STL), an expressive form of temporal properties of systems, to formulate task specifications and use black-box optimization (BBO) to adapt an LfD skill accordingly. We demonstrate our approach in simulation and on a real industrial setting using several tasks that showcase how our approach addresses the LfD limitations using STL and BBO.