Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning (2410.21845v2)

Abstract: Reinforcement learning (RL) holds great promise for enabling autonomous acquisition of complex robotic manipulation skills, but realizing this potential in real-world settings has been challenging. We present a human-in-the-loop vision-based RL system that demonstrates impressive performance on a diverse set of dexterous manipulation tasks, including dynamic manipulation, precision assembly, and dual-arm coordination. Our approach integrates demonstrations and human corrections, efficient RL algorithms, and other system-level design choices to learn policies that achieve near-perfect success rates and fast cycle times within just 1 to 2.5 hours of training. We show that our method significantly outperforms imitation learning baselines and prior RL approaches, with an average 2x improvement in success rate and 1.8x faster execution. Through extensive experiments and analysis, we provide insights into the effectiveness of our approach, demonstrating how it learns robust, adaptive policies for both reactive and predictive control strategies. Our results suggest that RL can indeed learn a wide range of complex vision-based manipulation policies directly in the real world within practical training times. We hope this work will inspire a new generation of learned robotic manipulation techniques, benefiting both industrial applications and research advancements. Videos and code are available at our project website https://hil-serl.github.io/.

Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning

The integration of reinforcement learning (RL) with human-in-the-loop methodologies presents an innovative framework for robotic manipulation, as demonstrated in this paper conducted by researchers from UC Berkeley. The paper introduces the Human-in-the-Loop Sample Efficient Robotic Learning (HIL-SERL) system, which leverages vision-based RL to acquire a variety of complex robotic manipulation skills. HIL-SERL tackles foundational challenges in robotic manipulation, such as dynamic interaction, precision, and multi-actuator coordination, using a concerted approach involving human intervention, state-of-the-art RL algorithms, and strategic system design.

Methodology and Key Contributions

One of the pivotal aspects of the proposed system is the integration of human interventions with RL training. By employing a pretrained visual backbone within the RL framework, the system addresses optimization stability, a common issue in vision-based RL settings. The system uses a sample-efficient off-policy RL algorithm based on RLPD, which incorporates human demonstrations and corrections, thus minimizing sample complexity—a persistent hurdle in real-world RL applications.

Human involvement is a significant component of the training process, providing corrective interventions during policy execution. These interventions aid in overcoming exploration inefficiencies inherent to RL, particularly for tasks that are challenging to initiate from scratch due to their complexity and the requirement for precise control strategies.

The system successfully addresses tasks previously deemed impractical to train with RL in real-world environments. It achieves a remarkable average improvement of 101% in success rates compared to imitation learning baselines and demonstrates a 1.8-fold increase in cycle speed. Noteworthy results are attained in challenging tasks like dynamic Jenga piece extraction, multi-arm coordination for timing belt assembly, and intricate object flipping. These tasks necessitate different control strategies, reflecting the flexibility and adaptiveness of the HIL-SERL framework.

Empirical Findings

The HIL-SERL framework achieves up to near-perfect success rates with relatively short training times of 1 to 2.5 hours. This efficiency is groundbreaking when considering the complexities involved in tasks such as dual-arm coordination and precision manipulation. The system not only outperforms imitation learning but also establishes that RL is viable for acquiring complex, vision-based manipulation policies directly in the real world within practical temporal constraints.

In terms of quantitative performance, the paper demonstrates that the policies trained using HIL-SERL significantly surpass those derived from imitation learning. For instance, the system learns to execute precision-intensive maneuvers like RAM insertion with improvements in success rate by 245%, whereas tasks like USB insertion see enhancements by 285%.

Theoretical and Practical Implications

Theoretically, this paper offers insights into the design of RL systems capable of real-world robotic manipulation tasks. It suggests the potential for RL to learn both reactive and predictive control strategies effectively. Practically, the results indicate a feasible pathway for deploying autonomous robotic systems in high-stakes environments like industrial assembly lines and high-mix low-volume production facilities, where adaptive, efficient skill acquisition is critical.

Future Directions

Despite the significant contributions, the research acknowledges certain limitations. Generalization across significantly varied environments and tasks, especially with longer horizons, remains an area ripe for exploration. Future research can focus on enhancing the adaptability and training efficiency of such systems, potentially through pretraining models for foundational manipulation skills or employing language-vision models for automatic task segmentation.

The paper's findings suggest transformative potential for robotic manipulation, positioning HIL-SERL as a pivotal step towards general-purpose, deployable robotic systems. As the field progresses, integrating broader datasets and leveraging foundation models could further extend the applicability of such RL frameworks to even more complex, autonomous manipulation tasks.