Whole-Body Ergodic Exploration with a Manipulator Using Diffusion

TL;DR

This paper introduces a novel whole-body ergodic exploration method for robotic manipulators that decomposes the robot into multiple agents, enabling efficient exploration of regions using ergodic control formalism and diffusion modeling.

Contribution

It extends ergodic control to whole-body manipulators with multiple sensors by decomposing the robot into kinematic agents and using a consensus-based control approach.

Findings

Enhanced exploration performance in terms of ergodicity.

Scales effectively to real-world robotic applications.

Demonstrated successful online exploration with a 7-axis robot.

Abstract

This paper presents a whole-body robot control method for exploring and probing a given region of interest. The ergodic control formalism behind such an exploration behavior consists of matching the time-averaged statistics of a robot trajectory with the spatial statistics of the target distribution. Most existing ergodic control approaches assume the robots/sensors as individual point agents moving in space. We introduce an approach that decomposes the whole-body of a robotic manipulator into multiple kinematically constrained agents. Then, we generate control actions by calculating a consensus among the agents. To do so, we use an ergodic control formulation called heat equation-driven area coverage (HEDAC) and slow the diffusion using the non-stationary heat equation. Our approach extends HEDAC to applications where robots have multiple sensors on the whole-body (such as tactile skin) and use all sensors to optimally explore the given region. We show that our approach increases the exploration performance in terms of ergodicity and scales well to real-world problems. We compare our method in kinematic simulations with the state-of-the-art and demonstrate the applicability of an online exploration task with a 7-axis Franka Emika robot. Additional material available at https://sites.google.com/view/w-ee-d/