Title: Behavior Cloning from Suboptimal Demonstrations with Robust World Models
Primary area: applications to robotics, autonomy, planning
Keywords: reinforcement learning, behavior cloning, world model

Abstract: Recent advances in behavior cloning and generative modeling of manipulation behaviors have shown promising results in learning complex multi-modal behavior distributions. However, a common limitation for all behavior cloning methods has been the challenge of acquiring high-quality training data. Existing state-of-the-art methods for policy learning face significant limitations when expert demonstrations are low quality, and often require the filtering or reweighting of failed or noisy demonstrations. To address this challenge, we propose an efficient offline reinforcement learning framework which utilizes an implicit world model to regularize a behavior cloning policy via predicted future returns. Our approach, Robust Imitation with a Critic (RIC), utilizes a critic-regularized imitation learning objective to incorporate both successful and failed demonstrations, steering imitation learning towards better trajectories via a conservative critic. Our method improves on prior works by accelerating the quality of learned policies by as much as 20% in the presence of suboptimal expert training data. Our simulated experiments consider different types of data suboptimality, including rollouts from a poor demonstrator policy and biased action perturbations from controller error.  We empirically evaluate different algorithmic choices for RIC, including comparisons of (1) offline reinforcement learning and behavior cloning, (2) critic guidance via an implicit world-model and a conservative critic estimate, and (3) different behavior cloning methods, including token and diffusion-based architectures.

TL;DR: Robustness approach to improve behavior cloning trained with suboptimal data via critic guidance