Robot Policy Learning

Robot policy learning aims to enable robots to autonomously learn effective behaviors, primarily through reinforcement learning and imitation learning techniques. Current research emphasizes improving sample efficiency by leveraging pre-trained models (e.g., vision foundation models, language models), incorporating human feedback, and utilizing advanced architectures like equivariant neural networks and diffusion models to handle complex data and improve generalization. These advancements are crucial for creating more robust and adaptable robots capable of performing diverse tasks in real-world environments, impacting fields ranging from manufacturing and healthcare to assistive robotics.