Satoshi Yamamori

M2oE: Modular Mixture of Experts for Multi-Morphology Reinforcement Learning of Modular Robots

Modular robots offer a promising solution for building versatile and adaptable robotic systems. For instance, space exploration robots can be designed to reconfigure to meet diverse task demands across varying environments. However, training such systems by Reinforcement Learning (RL) remains challe…

Motion Generation for Modular Robots Using Hierarchical Policies

When the Adversary Knows You Better: Adversarial Training for Learning-Based Legged Robots

Deep reinforcement learning has emerged as the dominant paradigm for training legged robots to locomote, however, when deployed in unstructured, dynamically varying real-world environments, the safety of neural network based controllers remains insufficiently guaranteed. Prior studies have demonstra…

VecNav: Vector Goal Robot Navigation from In-the-wild Videos

We propose VecNav, a novel approach that trains a monocular navigation model through self-supervision using uncalibrated, human-captured videos. These videos, characterized by unknown camera intrinsics and extrinsics, are readily available from video-sharing platforms (e.g. YouTube) and are referred