Building robotic systems that interact with the physical world, purposefully and intelligently.

GelSlim 5.0: Unifying Vision and Touch with a Single Fingertip Camera

A fingertip tactile sensor whose single camera either looks through its gel skin or reads contact, depending on the lighting, plus a model that fuses both views to estimate object pose and contact shape.

PhysiCalWorld: In-Context Physics Calibration for World Models

Lets a world model adapt to the physics of an unfamiliar scene, such as friction or mass, by watching a brief exploratory interaction and condensing it into a token that steers its predictions, with no fine-tuning.

A review of how touch is used in robot learning, from learned tactile representations and touch-aware policies to tactile foundation and world models, with open problems in transferring across sensors and robots.

A whole-body controller that lets a humanoid walk, carry, and push while bearing heavy loads, trained with reinforcement learning guided by a model of how the payload shifts the robot's balance.

Fixes simulation-trained dexterous hand policies that stumble on real hardware: a person physically guides only the struggling fingers, and those corrected runs, with fingertip force readings, train a stronger force-aware policy.

Moves touch-based policies from simulation to real tactile sensors without simulating those sensors, by training on contact signals the simulator already provides and then mapping real sensor readings into that learned space.