Orateur
Description
Title: Efficient Vision-Language-Action Model
Abstract: The talk asks how to make general-purpose robot policies usable without industrial-scale data or GPUs. Its answer is that low-resource robotics means choosing carefully where every parameter, demonstration and millisecond goes. It starts from first principles: a VLA is a policy that reads camera images, an instruction and the robot state, and outputs a short chunk of continuous actions. It is built from Transformer attention and a vision-language model. Our research, SmolVLA, pairs a compact pretrained VLM with a small flow-matching action expert. The result is a model about seven times smaller than π0 that is pretrained on community robot data. The talk then argues that the runtime is part of the policy. Asynchronous inference and real-time chunking keep the robot moving smoothly while the next action chunk is being computed. It closes on a next step, which is to learn how actions change the world (world-action models) without making the robot wait.
Bio: Adil Zouitine is a founding research scientist at UMA, where he works on general-purpose mobile and humanoid robots capable of learning in real time. He holds a PhD in robust reinforcement learning, supervised by Emmanuel Rachelson. His research focuses on robustness to disturbances, sample efficiency, generalization, and adversarial reinforcement learning.
After completing his PhD, Adil joined Hugging Face, where he helped build LeRobot, an open-source library for robot learning. He is also an active contributor to several open-source projects, including River, the online machine learning counterpart to scikit-learn.