Maxime Alvarez
Maxime Alvarez is a PhD student at the Matsuo-Iwasawa Laboratory, University of Tokyo, supervised by Professor Yutaka Matsuo and mentored by Tatsuya Matsushima, where his research focuses on generalist robotic policies, robot foundation models, and visual-language-action models. He concurrently works as a research engineer at NABLAS and as a robot foundation model engineer at Telexistence. Alvarez is best known as the lead author of the 2025 paper 'When Absolute State Fails: Evaluating Proprioceptive Encodings for Robust Manipulation,' which demonstrated that standard absolute joint-state representations fail critically — including causing dangerous robot movements — and that a simple episode-relative encoding scheme delivers dramatically improved task success in real-robot experiments.
“A deceptively simple fix — redefining a robot's starting position as 'zero' at the beginning of each episode — delivers a 15x improvement in task success rates over standard absolute state encoding”
Source→“the absolute encoding (Abs/Abs) achieved only a 5% task success rate in-distribution and 0% out-of-distribution, with the authors noting the OOD evaluation had to be halted entirely due to dangerous robot behavior”
Source→“the overwhelming majority of research aimed at closing this train-test distribution gap has focused on the visual domain... proprioceptive inputs, such as joint positions and velocities, are still frequently fed into neural policies as raw, absolute numeric values”
Source→“While this paper studies a specific task and a specific robot, the results are expected to hold with other robots in other settings that also have linear joints, such as the Agitbot G1, where the torso is set on a vertical rail”
Source→“robots are often equipped with mobile bases or linear rail systems to extend their operational workspace”
Source→AI-extracted from podcast / newsletter / paper summaries. May contain errors.