Robotics

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.

We introduce a new bipedal robot, designed with a focus on character-driven mechanical features.

We present a pipeline for fusing text and audio features to extract and re-use user information like names with the correct pronunciation.

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.