Robot Motion Diffusion Model: Motion Generation for Robotic Characters

Recent advancements in generative motion models have achieved remarkable
results, enabling the synthesis of lifelike human motions from textual
descriptions. These kinematic approaches, while visually appealing, often
produce motions that fail to adhere to physical constraints, resulting in artifacts
that impede real-world deployment. To address this issue, we introduce
a novel method that integrates kinematic generative models with physics based
character control. Our approach begins by training a reward surrogate t
o predict the performance of the downstream non-differentiable control
task, offering an efficient and differentiable loss function. This reward model
is then employed to fine-tune a baseline generative model, ensuring that
the generated motions are not only diverse but also physically plausible for real-world scenarios. The outcome of our processing is the Robot Motion
Diffusion Model (RobotMDM), a text-conditioned kinematic diffusion
model that interfaces with a reinforcement learning-based tracking controller.
We demonstrate the effectiveness of this method on a challenging
humanoid robot, confirming its practical utility and robustness in dynamic
environments.