Reactive Motion Generation on Learned Riemannian Manifolds

TL;DR

This paper introduces a novel approach for robot motion learning using learned Riemannian manifolds and geodesics, enabling natural motion generation and obstacle avoidance based on human demonstrations.

Contribution

It proposes a new method to learn Riemannian manifolds via a variational autoencoder for motion skills and obstacle-aware manifold reshaping for real-time obstacle avoidance.

Findings

Successfully learned and generated complex motion patterns from human demonstrations.

Demonstrated effective obstacle avoidance in task and joint space scenarios.

Generated multiple solution trajectories for complex tasks.

Abstract

In recent decades, advancements in motion learning have enabled robots to acquire new skills and adapt to unseen conditions in both structured and unstructured environments. In practice, motion learning methods capture relevant patterns and adjust them to new conditions such as dynamic obstacle avoidance or variable targets. In this paper, we investigate the robot motion learning paradigm from a Riemannian manifold perspective. We argue that Riemannian manifolds may be learned via human demonstrations in which geodesics are natural motion skills. The geodesics are generated using a learned Riemannian metric produced by our novel variational autoencoder (VAE), which is especially intended to recover full-pose end-effector states and joint space configurations. In addition, we propose a technique for facilitating on-the-fly end-effector/multiple-limb obstacle avoidance by reshaping the learned manifold using an obstacle-aware ambient metric. The motion generated using these geodesics may naturally result in multiple-solution tasks that have not been explicitly demonstrated previously. We extensively tested our approach in task space and joint space scenarios using a 7-DoF robotic manipulator. We demonstrate that our method is capable of learning and generating motion skills based on complicated motion patterns demonstrated by a human operator. Additionally, we assess several obstacle avoidance strategies and generate trajectories in multiple-mode settings.