Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning (2003.02637v1)

Abstract: Mobile manipulation is usually achieved by sequentially executing base and manipulator movements. This simplification, however, leads to a loss in efficiency and in some cases a reduction of workspace size. Even though different methods have been proposed to solve Whole-Body Control (WBC) online, they are either limited by a kinematic model or do not allow for reactive, online obstacle avoidance. In order to overcome these drawbacks, in this work, we propose an end-to-end Reinforcement Learning (RL) approach to WBC. We compared our learned controller against a state-of-the-art sampling-based method in simulation and achieved faster overall mission times. In addition, we validated the learned policy on our mobile manipulator RoyalPanda in challenging narrow corridor environments.

• The paper introduces an end-to-end RL approach that integrates base and manipulator control for efficient whole-body coordination.
• It utilizes Proximal Policy Optimization in PyBullet simulations to achieve real-time performance at 100Hz on a Raspberry Pi 3B.
• Real-world tests on the RoyalPanda demonstrate improved mission times over sampling-based methods, highlighting practical applicability.

Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning

The paper presented in this paper explores the development of a reinforcement learning (RL) approach for achieving whole-body control (WBC) of mobile manipulators. Traditional methods in mobile manipulation often decouple the movements of the base and manipulator, which can limit efficiency and the effective workspace of the robot. The authors aim to overcome these limitations by employing an end-to-end RL strategy designed to handle the challenges associated with WBC.

Key Contributions

1. Learning-Based Approach to WBC: The paper introduces an RL-based framework that can operate online, efficiently processing sensory data and executing control commands in real-time. Notably, the approach runs at a frequency of at least 100 Hz on a Raspberry Pi 3B, showcasing its applicability in resource-constrained environments.
2. Comparison with Sampling-Based Methods: The paper provides a comparative analysis of the RL-based approach against a state-of-the-art sampling-based method, demonstrating improved mission times in simulation environments.
3. Real-World Validation: The learned RL policies were validated on a real mobile manipulator, RoyalPanda, in complex environments, thereby underscoring the practical adaptability of the method.

Methodology

The approach employs RL techniques, particularly Proximal Policy Optimization (PPO), to train the manipulation policies. The training was conducted in a simulated environment designed using PyBullet, with varying corridor layouts to ensure that the system could generalize well across different scenarios. The state space includes two LiDAR scans, joint positions, velocities, and the target position relative to the end-effector. Actions consist of accelerations for both the base and the joints, discretized into a fixed number of levels.

Reward Design

The reward function is a composite metric that encourages reaching the target swiftly while avoiding collisions. It penalizes time taken, deviation from a precomputed harmonic potential path, and incurs penalties for collisions or reaching joint limits. Such a design aims to balance task completion speed with safety and efficiency.

Simulation and Real-World Results

In the simulation experiments, the RL approach exhibited shorter mission times than sampling-based RRTConnect, despite occasionally longer trajectory paths due to the absence of explicit path length penalties in the reward design. The success rate in highly constrained environments showcased potential for improvement, indicating a need for further refinement in dynamic adjustment of safety margins or enhanced perception integration.

In real-world tests, the RL policies demonstrated effective transfer from simulation to reality but faced challenges with unmodeled environmental factors. This underscores the necessity for better sensor integration and possibly memory mechanisms to account for occluded obstacles.

Implications and Future Directions

The paper suggests that RL can serve as a viable alternative to classical planning and control paradigms, especially in dynamic and partially known environments. The successful deployment on a real platform marks a significant step towards practical applications of RL in robotics. Future work might focus on incorporating richer sensory inputs and exploring attention-based or recurrent neural architectures to enhance obstacle detection and avoidance capabilities.

By pushing the boundaries of RL in the context of WBC, this research contributes to the ongoing discourse on the integration of learning methods in robotics, with implications for improving the autonomy and versatility of automated systems. Further exploration into hybrid approaches leveraging both model-based and learning-based strategies could offer insights into optimizing control in varying operational contexts.