Robotics

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 to 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.

In this paper, we propose a technique that simultaneously solves for optimal design and control parameters for a robotic character whose design is parameterized with configurable joints. At the technical core of our technique is an efficient solution strategy that uses dynamic programming to solve for optimal state, control, and design parameters, together with a strategy to remove redundant constraints that commonly exist in general robot assemblies with kinematic loops.

We propose to augment common simulation representations with a transformer-inspired architecture, by training a network to predict the true state of robot building blocks given their simulation state. Because we augment building blocks, rather than the full simulation state, we make our approach modular which improves generalizability and robustness.

Legged robots are designed to perform highly dynamic motions. However, it remains challenging for users to retarget expressive motions onto these complex systems. In this paper, we present a Differentiable Optimal Control (DOC) framework that facilitates the transfer of rich motions from either animals or animations onto these robots.

We present ongoing work to develop a robust and natural turn-taking behavior for a social agent to engage a dynamically changing group in a conversation.

Robots with kinematic loops are known to have superior mechanical performance. However, due to these loops, their modeling and control is challenging, and prevents a more widespread use. In this paper, we describe a versatile Inverse Kinematics (IK) formulation for the retargeting of expressive motions onto mechanical systems with loops.