Combining manipulation with the mobility of legged robots is essential for a wide range of robotic applications. However, integrating an arm with a mobile base significantly increases the system's complexity, making precise end-effector control challenging. Existing model-based approaches are often constrained by their modeling assumptions, leading to limited robustness. Meanwhile, recent Reinforcement Learning (RL) implementations restrict the arm's workspace to be in front of the robot or track only the position to obtain decent tracking accuracy. In this work, we address these limitations by introducing a whole-body RL formulation for end-effector pose tracking in a large workspace on rough, unstructured terrains. Our proposed method involves a terrain-aware sampling strategy for the robot's initial configuration and end-effector pose commands, as well as a game-based curriculum to extend the robot's operating range. We validate our approach on the ANYmal quadrupedal robot with a six DoF robotic arm. Through our experiments, we show that the learned controller achieves precise command tracking over a large workspace and adapts across varying terrains such as stairs and slopes. On deployment, it achieves a pose-tracking error of 2.64 cm and 3.64°, outperforming existing competitive baselines. The video of our work is available at: wholebody-pose-tracking.

Controlling the contact force during interactions is an inherent requirement for locomotion and manipulation tasks. Current reinforcement learning approaches to locomotion and manipulation rely implicitly on forceful interaction to accomplish tasks but do not explicitly regulate it. This paper proposes a reinforcement learning task specification that focuses on matching desired contact force levels. Integrating force control with the coordination of a robot’s body and arm, we present an end-to-end policy for legged manipulator control. Force control enables us to realize compliant gripper and whole-body pulling movements that have not been previously demonstrated using a learned policy. It also facilitates a characterization of the force-tracking performance of learned policies in simulation and the real world, indicating their performance potential for force-critical tasks. Video is available at the project website: https://tif-twirl-13.github.io/learning-compliance.