Elastic Motion Policy: An Adaptive Dynamical System for Robust and Efficient One-Shot Imitation Learning

Abstract

Behavior cloning (BC) has become a staple imitation learning paradigm in robotics due to its ease of teaching robots complex skills directly from expert demonstrations. However, BC suffers from an inherent generalization issue. To solve this, the status quo solution is to gather more data. Yet, regardless of how much training data is available, out-of-distribution performance is still sub-par, lacks any formal guarantee of convergence and success, and is incapable of allowing and recovering from physical interactions with humans. These are critical flaws when robots are deployed in ever-changing human-centric environments. Thus, we propose Elastic Motion Policy (EMP), a one-shot imitation learning framework that allows robots to adjust their behavior based on the scene change while respecting the task specification. Trained from a single demonstration, EMP follows the dynamical systems paradigm where motion planning and control are governed by first-order differential equations with convergence guarantees. We leverage Laplacian editing in full end-effector space, $\mathbb{R}^3\times SO(3)$, and online convex learning of Lyapunov functions, to adapt EMP online to new contexts, avoiding the need to collect new demonstrations. We extensively validate our framework in real robot experiments, demonstrating its robust and efficient performance in dynamic environments, with obstacle avoidance and multi-step task capabilities. Project Website: https://elastic-motion-policy.github.io/EMP/