7 accepted papers
In typical robot learning, deep reinforcement learning policies are employed in the upper control layer to generate target joint angles for robot motion, while conventional controllers are used in the fast lower control layer to control each joint motor. This paper presents a fully neural network-ba…
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…
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…
In typical robot learning, deep reinforcement learning policies are employed in the upper control layer to generate target joint angles for robot motion, while conventional controllers are used in the fast lower control layer to control each joint motor. This paper presents a fully neural network-ba
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
In this study, we propose a novel human-in-the-loop optimization approach for exoskeleton robot control. We develop a method to optimize widely-used Electromyography (EMG)-based assistive strategies. If we use multiple EMG channels to control multi-DoF robots, optimization process becomes complex an…