Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer (1609.07088v1)

Abstract: Reinforcement learning (RL) can automate a wide variety of robotic skills, but learning each new skill requires considerable real-world data collection and manual representation engineering to design policy classes or features. Using deep reinforcement learning to train general purpose neural network policies alleviates some of the burden of manual representation engineering by using expressive policy classes, but exacerbates the challenge of data collection, since such methods tend to be less efficient than RL with low-dimensional, hand-designed representations. Transfer learning can mitigate this problem by enabling us to transfer information from one skill to another and even from one robot to another. We show that neural network policies can be decomposed into "task-specific" and "robot-specific" modules, where the task-specific modules are shared across robots, and the robot-specific modules are shared across all tasks on that robot. This allows for sharing task information, such as perception, between robots and sharing robot information, such as dynamics and kinematics, between tasks. We exploit this decomposition to train mix-and-match modules that can solve new robot-task combinations that were not seen during training. Using a novel neural network architecture, we demonstrate the effectiveness of our transfer method for enabling zero-shot generalization with a variety of robots and tasks in simulation for both visual and non-visual tasks.

Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer

The paper presents an innovative approach to reinforcement learning (RL) called Modular Policy Networks (MPNs), explicitly designed for enhancing multi-task and multi-robot transfer. It highlights the challenge of considerable data requirements and manual engineering efforts in conventional RL, proposing MPNs as a solution by using neural networks decomposed into task-specific and robot-specific modules. This modular decomposition enables the sharing of information more effectively across various combinations of tasks and robots, promoting the reuse of learned skills and facilitating zero-shot generalization.

Key Contributions

1. Modular Decomposition: The paper introduces an architecture that splits neural network policies into modular components. Each robot-specific module deals with the intrinsic dynamics and kinematics characteristic of the robot, while each task-specific module focuses on task-related aspects such as perception or strategy. This separation aids in the creation of "mix-and-match" policies that can be recombined to address new tasks or robots not encountered during initial training.
2. Zero-Shot Generalization and Efficient Learning: By adopting this modularity, MPNs demonstrate the ability to perform zero-shot generalization for unseen task-robot combinations. Additionally, where zero-shot is not fully feasible, the approach offers significantly faster learning due to effective initialization based on previously learned components, as shown in experiments with simulated environments using the MuJoCo physics engine.
3. Regularization Techniques: The paper discusses techniques such as dropout and limiting interface dimensionalities to ensure that modules learn generalizable interfaces, discouraging overfitting to individual robot-task configurations. These regularization methods contribute to stable training and robust transfer capabilities.

Experimental Evaluation

The empirical evaluation was conducted on various simulated tasks, including object manipulation scenarios and vision-based tasks with significant environmental variability. The results demonstrate that MPNs can effectively decompose and recombine learned policies to handle new combinations of tasks and robots. Numerical results highlight the method's superiority in terms of task completion and learning speed compared to baseline approaches, such as single-policy learning for each task-robot instance.

1. Object Manipulation Tasks: Tasks involving pushing blocks and drawers were tested with different robot arm configurations. MPNs showcased faster convergence on learning tasks by leveraging previously unseen task-robot combinations.
2. Vision Tasks: The effectiveness of MPNs for transferring visual perception tasks was validated through experiments involving variously colored blocks, where new robot-task combinations were capable of performing well in a zero-shot manner.

Implications and Future Directions

The modular approach proposed in this paper holds significant promise for advancing RL techniques, particularly in domains where multiple agents or tasks coexist, such as industrial automation or collaborative robotics. The capability to handle new combinations of tasks and robots without extensive retraining is a step forward in achieving more generalizable and reusable models in robotics.

Future directions might include exploring asynchronous training methods, scale adaptation for larger and more complex systems, and potential integration with lifelong learning paradigms. Additionally, extending MPNs to real-world applications and testing with diverse robot types and environments could further validate their practical efficacy and robustness.

In conclusion, the MPN framework presents a compelling strategy for addressing the challenges of multi-task and multi-robot RL, pushing the envelope towards more intelligent and adaptable robotic systems. Its successful operationalization in various simulated environments underscores its potential for broad applicability in AI and robotics research.