ROJan 5, 2021

Composable Geometric Motion Policies using Multi-Task Pullback Bundle Dynamical Systems

arXiv:2101.01297v216 citationsHas Code
Originality Highly original
AI Analysis

This work provides a fast and easy-to-use framework for generating motion policies without unwanted local minima on general manifolds, benefiting researchers and practitioners in robotics.

This paper introduces a framework for designing and fusing robot task behaviors into stable motion policies on non-Euclidean manifolds, addressing challenges in reactive planning and control. The framework simplifies individual task design and modular composition, demonstrating performance at 300-500 Hz on a manipulator arm.

Despite decades of work in fast reactive planning and control, challenges remain in developing reactive motion policies on non-Euclidean manifolds and enforcing constraints while avoiding undesirable potential function local minima. This work presents a principled method for designing and fusing desired robot task behaviors into a stable robot motion policy, leveraging the geometric structure of non-Euclidean manifolds, which are prevalent in robot configuration and task spaces. Our Pullback Bundle Dynamical Systems (PBDS) framework drives desired task behaviors and prioritizes tasks using separate position-dependent and position/velocity-dependent Riemannian metrics, respectively, thus simplifying individual task design and modular composition of tasks. For enforcing constraints, we provide a class of metric-based tasks, eliminating local minima by imposing non-conflicting potential functions only for goal region attraction. We also provide a geometric optimization problem for combining tasks inspired by Riemannian Motion Policies (RMPs) that reduces to a simple least-squares problem, and we show that our approach is geometrically well-defined. We demonstrate the PBDS framework on the sphere $\mathbb S^2$ and at 300-500 Hz on a manipulator arm, and we provide task design guidance and an open-source Julia library implementation. Overall, this work presents a fast, easy-to-use framework for generating motion policies without unwanted potential function local minima on general manifolds.

Code Implementations1 repo
Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes