Composing Diffusion Policies for Few-shot Learning of Movement Trajectories

TL;DR

This paper introduces DSE, a probabilistic compositional approach using diffusion policies for few-shot learning of robot movement trajectories, enabling rapid skill acquisition with minimal demonstrations.

Contribution

The paper presents DSE, a novel method for compositional diffusion policies that improves few-shot learning of robot trajectories and introduces a new evaluation metric, MMD-FK.

Findings

Achieved over 30% reduction in MMD-FK error across skills and demonstrations.

Successfully taught novel trajectories to a robot with only 5 demonstrations.

Demonstrated real-world applicability of the approach in robot skill learning.

Abstract

Humans can perform various combinations of physical skills without having to relearn skills from scratch every single time. For example, we can swing a bat when walking without having to re-learn such a policy from scratch by composing the individual skills of walking and bat swinging. Enabling robots to combine or compose skills is essential so they can learn novel skills and tasks faster with fewer real world samples. To this end, we propose a novel compositional approach called DSE- Diffusion Score Equilibrium that enables few-shot learning for novel skills by utilizing a combination of base policy priors. Our method is based on probabilistically composing diffusion policies to better model the few-shot demonstration data-distribution than any individual policy. Our goal here is to learn robot motions few-shot and not necessarily goal oriented trajectories. Unfortunately we lack a general purpose metric to evaluate the error between a skill or motion and the provided demonstrations. Hence, we propose a probabilistic measure - Maximum Mean Discrepancy on the Forward Kinematics Kernel (MMD-FK), that is task and action space agnostic. By using our few-shot learning approach DSE, we show that we are able to achieve a reduction of over 30% in MMD-FK across skills and number of demonstrations. Moreover, we show the utility of our approach through real world experiments by teaching novel trajectories to a robot in 5 demonstrations.