Robotics

This is an early status update on my current project for Advanced Deep Learning for Robotics. The broader question is whether tactile information can make online adaptation more useful for in-hand manipulation. The project is inspired by RMA-style adaptation: train with access to hidden information about the environment, then learn to infer a useful latent representation from recent history at test time. The comparison we eventually care about is simple to state: if the robot only gets proprioceptive history, how much can it adapt, and what changes when we also give it tactile or contact information? ...

At EPFL RoboHack 2026 in Lausanne, Switzerland, our team worked on a physical robot arm challenge: build the arm, record demonstrations, train a policy, and then see whether it could perform a precise manipulation task on the real hardware. The short version is that we built a leader-follower setup, collected teleoperated demonstrations, and trained an imitation-learning policy to pick up a small custom object, insert it into a matching hole, and turn it into place. It was exactly the kind of robotics project where the software only makes sense once the hardware, calibration, cameras, gripper, and task design all work well enough at the same time. ...