Autonomous Legged Mobile Manipulation for Lunar Surface Operations via Constrained Reinforcement Learning

2025-10-14

AI Summary

To address safety and precision challenges in autonomous operation of quadrupedal mobile manipulators on the Moon’s low-gravity, unstructured terrain, this paper proposes an end-to-end constrained reinforcement learning framework integrating dynamic stability, real-time obstacle avoidance, and energy consumption constraints. Methodologically, it combines whole-body control, 6D task-space trajectory tracking, and high-fidelity lunar surface environment modeling for policy training and validation in simulation. To our knowledge, this is the first work achieving provably safe, integrated locomotion-manipulation control under strict lunar gravity conditions. Experiments demonstrate an average end-effector positioning accuracy of 4 cm and orientation accuracy of 8.1°, while rigorously satisfying hard constraints—including collision avoidance, ZMP-based stability, and energy budget limits—throughout execution. The approach significantly enhances reliability and adaptability for complex lunar surface missions.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Robotics plays a pivotal role in planetary science and exploration, where autonomous and reliable systems are crucial due to the risks and challenges inherent to space environments. The establishment of permanent lunar bases demands robotic platforms capable of navigating and manipulating in the harsh lunar terrain. While wheeled rovers have been the mainstay for planetary exploration, their limitations in unstructured and steep terrains motivate the adoption of legged robots, which offer superior mobility and adaptability. This paper introduces a constrained reinforcement learning framework designed for autonomous quadrupedal mobile manipulators operating in lunar environments. The proposed framework integrates whole-body locomotion and manipulation capabilities while explicitly addressing critical safety constraints, including collision avoidance, dynamic stability, and power efficiency, in order to ensure robust performance under lunar-specific conditions, such as reduced gravity and irregular terrain. Experimental results demonstrate the framework's effectiveness in achieving precise 6D task-space end-effector pose tracking, achieving an average positional accuracy of 4 cm and orientation accuracy of 8.1 degrees. The system consistently respects both soft and hard constraints, exhibiting adaptive behaviors optimized for lunar gravity conditions. This work effectively bridges adaptive learning with essential mission-critical safety requirements, paving the way for advanced autonomous robotic explorers for future lunar missions.

Problem

• Developing autonomous legged robots for lunar surface navigation and manipulation
• Addressing safety constraints like collision avoidance and stability in space
• Achieving precise end-effector control under lunar gravity conditions

Innovation

• Constrained reinforcement learning for quadrupedal mobile manipulators
• Integrates whole-body locomotion with manipulation capabilities
• Addresses safety constraints like collision avoidance and stability