Constrained Behavior Cloning for Robotic Learning

Abstract

Behavior cloning (BC) is a widely used method for learning from expert demonstrations due to its simplicity and efficiency. However, the reliability and stability of BC decline when facing data distribution shifts, especially in single-arm robots with limited fields of view. This study introduces a Geometrically and Historically Constrained Behavior Cloning (GHCBC) method, where an HCBC module utilizes visual and action histories to capture temporal dependencies, maximizing the use of available information, and a GCBC module incorporates high-level perceptual data, such as the relative poses of joints and end-effectors, to enhance BC performance. Experiments demonstrate that the GHCBC outperforms current SOTA BC methods, achieving a 31.5% improvement in simulation success rates and 48.4% in real-robot scenarios respectively. To the best of our knowledge, this is the first time that the GHCBC has been introduced in robotic BC where great potential is demonstrated for long-term tasks in real world environments.