Abstract:

Non-prehensile manipulation is challenging due to complex contact interactions between objects, the environment, and robots. Model-based approaches can efficiently generate complex trajectories of robots and objects under contact constraints. However, they tend to be sensitive to model inaccuracies and require access to privileged information (e.g., object mass, size, pose), making them less suitable for novel objects. In contrast, learning-based approaches are typically more robust to modeling errors but require large amounts of data. In this paper, we bridge these two approaches to propose a framework for learning closed- loop non-prehensile manipulation. By leveraging computationally efficient Contact-Implicit Trajectory Optimization (CITO), we design demonstration-guided deep Reinforcement Learning (RL), leading to sample-efficient learning. We also present a sim- to-real transfer approach using a privileged training strategy, enabling the robot to perform non-prehensile manipulation using only proprioception, vision, and force sensing without access to privileged information. Our method is evaluated on several tasks, demonstrating that it can successfully perform zero-shot sim-to- real transfer.