Legs-as-Arms: A Multitask Reinforcement Learning Framework for Simultaneous Loco-Manipulation in Hexapod Robots

Legged robots are designed to perform operational tasks in complex, rugged terrains. These tasks generally encompass two fundamental modalities: locomotion and manipulation. Humanoid robots, benefiting from their distinctive morphological configuration (independent arm and leg structures), can decouple the control of these two task types. In contrast, conventional quadruped/hexapod robots, lacking dedicated end effectors, are typically optimized for locomotion. While manipulation capabilities can be added via auxiliary robotic arms, this approach leads to unnecessary energy consumption and sacrifices payload capacity. Adopting a limb reuse architecture offers a promising solution. This work proposes Legs-as-Arms, a multitask reinforcement learning framework for hexapod robots that enables simultaneous locomotion and manipulation. The framework implements effective limb reuse, enabling any leg(s) to track desired trajectories while maintaining stable body movement. By leveraging a multihead critic network and value normalization, we integrate three key capabilities into a single control policy: 1) base locomotion control; 2) stationary whole-body manipulation; and 3) dynamic mobile manipulation. Our method demonstrates fault-tolerant gaits, workspace expansion, centimeter-level trajectory tracking accuracy, and significant load adaptability. We validate the framework’s effectiveness and practical application value through comprehensive hardware experiments across diverse multimodal scenarios.