Reinforcement and Imitation Learning for Diverse Visuomotor Skills (1802.09564v2)

Abstract: We propose a model-free deep reinforcement learning method that leverages a small amount of demonstration data to assist a reinforcement learning agent. We apply this approach to robotic manipulation tasks and train end-to-end visuomotor policies that map directly from RGB camera inputs to joint velocities. We demonstrate that our approach can solve a wide variety of visuomotor tasks, for which engineering a scripted controller would be laborious. In experiments, our reinforcement and imitation agent achieves significantly better performances than agents trained with reinforcement learning or imitation learning alone. We also illustrate that these policies, trained with large visual and dynamics variations, can achieve preliminary successes in zero-shot sim2real transfer. A brief visual description of this work can be viewed in https://youtu.be/EDl8SQUNjj0

• The paper introduces a hybrid framework that combines reinforcement and imitation rewards to efficiently learn visuomotor skills.
• It utilizes curriculum initialization from demonstration states to ease exploration in long-horizon robotic tasks.
• Empirical results show significant sim2real transfer improvements across tasks like block lifting and liquid pouring.

Overview of Reinforcement and Imitation Learning for Diverse Visuomotor Skills

The paper by Zhu et al. proposes an advanced approach to learning diverse visuomotor skills using a hybrid model of reinforcement learning (RL) and imitation learning (IL). This method specifically focuses on robotic manipulation tasks, employing end-to-end policies that transform RGB camera inputs directly into joint velocities. This approach not only strives to alleviate the arduous process of engineering scripted robotics controllers for complex tasks but also demonstrates improvements in zero-shot sim2real transfer, a challenging aspect in the robotics field.

Key Contributions

The paper introduces a model-free deep reinforcement learning framework that incorporates a small number of human demonstration data to improve learning efficiency and task performance. Meanwhile, the proposed method leverages several innovative techniques, including leveraging privileged state information during training and creating a curriculum of demonstration-based states, which aids in reducing the difficulty of exploration in continuous domains.

Key innovations include:

1. Hybrid Reward Structure: This structure combines traditional task rewards with imitation-based rewards derived from Generative Adversarial Imitation Learning (GAIL). This hybrid approach balances exploration and imitation, benefiting from both unsupervised exploration techniques of RL and the guided learning trajectory of IL.
2. Curriculum Initialization: By starting training episodes from states derived from demonstration trajectories, the framework efficiently reduces the exploration burden in long-horizon tasks.
3. Sim2Real Transfer: The method applies simulation training with domain randomization and demonstrates successful preliminary results in transferring learned policies to real-world robotic tasks without further fine-tuning.
4. Use of Privileged Information: During training in simulation, the algorithm utilizes full state information for learning value functions, even though such information would not be available during real-world deployment.

Empirical Results

The experiments conducted on multiple robotic manipulation tasks demonstrate significant improvements, with the hybrid RL and IL model outperforming both standalone RL and IL approaches. For instance, the experimental tasks — ranging from simple block lifting to the more complex task of pouring liquid — illustrate the robustness of this model across diverse scenarios. Notably, for the more challenging tasks, the hybrid approach achieves higher returns compared to models that only employed reinforcement learning or imitation.

Implications and Future Directions

This research has important implications for the development of robotic systems capable of performing complex manipulation tasks in dynamic environments. The hybrid framework streamlines the learning process, dampening the impact of state and action space exploration challenges. Practically, improved sim2real transfer without exhaustive fine-tuning holds promise for deploying robots in practical applications efficiently.

From a theoretical perspective, these findings may pave the way for more effective integration of supervised demonstration data within deep reinforcement learning frameworks, enhancing learning efficiency and generalization capabilities. Future research could focus on optimizing the balance between imitation and reinforcement rewards, as well as expanding the application of this framework to more intricate robotic tasks and environments, potentially extending to multi-agent systems or hierarchical task learning scenarios.

In summary, Zhu et al.'s paper makes notable strides in bridging the gap between simulated training and real-world robotic application, suggesting robust strategies that engineers and researchers can adopt to develop more versatile and adaptive robotic systems.