# Bipedal Loco-Manipulation

This work presents a unified reinforcement-learning whole-body controller that takes a single 6-DoF end-effector target as the sole task-level command and directly outputs coordinated actions for a bipedal base and a six-joint arm across 14 joints, reaching 88.30% task success in simulation (2.85 cm mean position error, 5.38 cm P95), and on a real bipedal platform reusing the same controller across VR teleoperation, a learned diffusion policy, and scripted trajectories while extending vertical reach from roughly 38-163 cm for a floating-base plus inverse-kinematics baseline to roughly 3-191 cm.

This work presents a unified reinforcement-learning whole-body controller that takes a single 6-DoF end-effector target as the sole task-level command and directly outputs coordinated actions for a bipedal base and a six-joint arm across 14 joints, reaching 88.30% task success in simulation (2.85 cm mean position error, 5.38 cm P95), and on a real bipedal platform reusing the same controller across VR teleoperation, a learned diffusion policy, and scripted trajectories while extending vertical reach from roughly 38-163 cm for a floating-base plus inverse-kinematics baseline to roughly 3-191 cm.