Imitation of Manipulation Skills Using Multiple Geometries

TL;DR

This paper introduces a learning method that automatically selects optimal geometric representations from multiple coordinate systems to improve robotic manipulation tasks, enabling better generalization and task invariance.

Contribution

It proposes an extension of Gaussian distributions on Riemannian manifolds to analyze demonstrations across multiple geometries for robotic manipulation.

Findings

Robots can exploit multiple geometries for manipulation tasks.

The approach generalizes to new situations while preserving task invariants.

Effective in simulation and real robot experiments.

Abstract

Daily manipulation tasks are characterized by geometric primitives related to actions and object shapes. Such geometric descriptors are poorly represented by only using Cartesian coordinate systems. In this paper, we propose a learning approach to extract the optimal representation from a dictionary of coordinate systems to encode an observed movement/behavior. This is achieved by using an extension of Gaussian distributions on Riemannian manifolds, which is used to analyse a set of user demonstrations statistically, by considering multiple geometries as candidate representations of the task. We formulate the reproduction problem as a general optimal control problem based on an iterative linear quadratic regulator (iLQR), where the Gaussian distribution in the extracted coordinate systems are used to define the cost function. We apply our approach to object grasping and box opening tasks in simulation and on a 7-axis Franka Emika robot. The results show that the robot can exploit several geometries to execute the manipulation task and generalize it to new situations, by maintaining the invariant characteristics of the task in the coordinate system(s) of interest.