Training a physics-based third-person character controller with residual RL + mocap
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一位开发者正在Unity中尝试使用强化学习来构建一个自平衡、物理驱动的第三人称角色控制器。该人形角色使用ArticulationBody关节构建，并通过Unity ML-Agents进行训练。策略并非从零开始学习运动，而是利用动作捕捉驱动的运动学角色作为参考轨迹。…
I’ve been experimenting with using reinforcement learning to build a self-balancing, physics-driven third-person character controller in Unity.
The character is a humanoid built with ArticulationBody joints and trained with Unity ML-Agents. Instead of asking the policy to learn locomotion entirely from scratch, I use a mocap-driven kinematic character as the reference trajectory.
The physics character continuously tracks the reference pose, while the policy outputs residual joint corrections on top of the mocap targets. The idea is that the animation provides the underlying gait, while RL learns the smaller corrections needed for balance, momentum control, foot placement, and recovery.
The reward currently combines things like joint-pose imitation, end-effector tracking, COM/root velocity, orientation, height, and foot-contact agreement, with additional penalties for excessive actions, jitter, and abrupt action changes.