Robotics

At Europe Embodied 2026 in Munich, we tried to train a Franka Panda arm to disassemble disposable vapes. The physical task was simple to describe. A vape sits in a printed holder. The robot has custom printed gripper fingers. The fingers pinch the back cap and pull upward, so the battery can be removed from the vape instead of going into shredded waste. The learned RL policy stayed in simulation. We did not deploy it on the real Franka during the hackathon. ...

This is a status update on my current project for Advanced Deep Learning for Robotics. The broader question is still 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 the useful parts of that information from recent history at test time. The comparison I eventually care about is simple to state: if the controller 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. ...