Projects

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. ...

This project was part of a small group benchmark on foundation models for multiplexed fluorescence microscopy. The general question was simple: if we take recent pretrained models and run them on real multiplexed microscopy data, what do their embeddings actually capture? My part focused on a channel-agnostic masked autoencoder, or CA-MAE, from the paper Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology. I did not train a new foundation model here, and I did not fine-tune the model. The work was more practical than that: take a pretrained model, make our data fit the input format, run inference on the university cluster, and then look at the resulting embeddings carefully. ...

This project started as a practical follow-up to an introductory deep reinforcement learning course. The goal was simple to state and harder to make work: train deep RL agents to solve a small stochastic pickup-and-delivery task better than a hand-written greedy baseline. The code is available on GitHub: github.com/jclotten/deep-rl-gridworld-benchmark. Why This Project Small gridworlds are often used to explain reinforcement learning because the rules are easy to understand. That does not mean they are automatically easy for deep RL. ...