Learning Navigation Behaviors End-to-End with AutoRL (1809.10124v2)

Abstract: We learn end-to-end point-to-point and path-following navigation behaviors that avoid moving obstacles. These policies receive noisy lidar observations and output robot linear and angular velocities. The policies are trained in small, static environments with AutoRL, an evolutionary automation layer around Reinforcement Learning (RL) that searches for a deep RL reward and neural network architecture with large-scale hyper-parameter optimization. AutoRL first finds a reward that maximizes task completion, and then finds a neural network architecture that maximizes the cumulative of the found reward. Empirical evaluations, both in simulation and on-robot, show that AutoRL policies do not suffer from the catastrophic forgetfulness that plagues many other deep reinforcement learning algorithms, generalize to new environments and moving obstacles, are robust to sensor, actuator, and localization noise, and can serve as robust building blocks for larger navigation tasks. Our path-following and point-to-point policies are respectively 23% and 26% more successful than comparison methods across new environments. Video at: https://youtu.be/0UwkjpUEcbI

• The paper’s main contribution is the AutoRL framework that automates the search for optimal reward functions and network architectures to enhance reinforcement learning for navigation.
• It reports robust improvements with a 26% increase in success for point-to-point navigation and 23% for path-following tasks compared to baseline methods.
• The framework outperforms manual tuning and traditional planning methods, demonstrating resilience against sensor noise and dynamic obstacles in varied environments.

A Comprehensive Exploration of End-to-End Learning for Robot Navigation with AutoRL

This essay examines a paper that introduces AutoRL, a method designed to enhance the learning of navigation behaviors in robotics through reinforcement learning (RL). The paper's primary contribution lies in its novel approach of automating the search for optimal reward functions and neural network architectures, which are crucial components for effective RL training. The paper emphasizes the applicability of AutoRL in two fundamental navigation tasks: point-to-point (P2P) movement and path-following (PF). Both of these tasks demand a robot to navigate through an environment while avoiding static and dynamic obstacles, making them essential building blocks in autonomous navigation systems.

The authors implement the AutoRL framework as an evolutionary automation layer around deep RL. This is achieved by optimizing hyperparameters through large-scale automation, first focusing on finding the reward function that maximizes task completion and then determining a neural network architecture that enhances cumulative reward outcomes. This strategic sequencing of optimization goals allows the system to hone in on rewards and network configurations that directly impact the RL process.

A significant portion of the paper is devoted to empirical evaluation, demonstrating the efficacy of AutoRL in producing robust, transferable policies. The trained models are validated in simulation environments and on physical robots to assess their generalization capabilities. Notably, the policies developed using AutoRL outperform several baseline methods, including manually-tuned RL and traditional motion planning techniques like Artificial Potential Fields (APF) and Dynamic Window Approach (DWA), as well as RL approaches such as PRM-RL.

Key Results and Claims

The results highlight AutoRL's ability to overcome challenges commonly associated with RL, such as catastrophic forgetfulness, a phenomenon where learned behaviors are lost when new information is introduced during training. The paper claims substantial improvements in task success rates for both P2P and PF policies, with reported increases of 26% and 23% respectively. This improvement underscores the robustness of the AutoRL framework in terms of task generalization across varying and complex scenarios.

AutoRL not only proves to be more successful in static environments but also shows resilience against noise and dynamic obstacles, which are often unpredictable. The policies developed through this method are robust against sensor and actuator noise, as shown by thorough testing across different environments scaled up from the training conditions.

Practical and Theoretical Implications

From a practical standpoint, AutoRL has significant implications for the field of robotics, particularly in applications requiring autonomous navigation, such as logistics, assistive robots, and service robots. By automating the challenging aspects of RL training, AutoRL could reduce the need for extensive manual tuning, making the development of intelligent navigation systems more accessible and efficient.

Theoretically, the use of evolutionary strategies for optimizing both reward functions and network architectures within the context of RL sets a precedent for future research. It encourages a broader exploration of gradient-free optimization methods in scenarios where traditional gradient-based approaches might struggle due to sparse rewards or complex dynamical models.

Speculation on Future Developments

The integration of AutoRL into more complex or higher-dimensional tasks, including mobile manipulation in more dynamic unstructured environments, presents an exciting avenue for future research. Exploring hybrid models that combine AutoRL with other machine learning paradigms could also yield insights that further enhance robot autonomy.

In conclusion, the paper provides a robust foundation for leveraging automated optimization in RL tasks, demonstrating significant advancements in both the theoretical understanding and practical application of navigation behaviors in robotics. By streamlining the development process, AutoRL holds the promise of accelerating progress toward truly autonomous robotic systems capable of efficient and intelligent navigation in our dynamically changing world.