17 citations · 27 across the 13 of their papers we have counts for
13 papers
Diffusion Policy for Coordinated Control of a Nonholonomic Mobile Base and Dual Arms in Door Opening and Passing
Shangqun Yu, Matthew En, Daniel Wu +4
Opening heavy, self closing doors, especially those that require pulling remains a long standing challenge in robotics. Humans naturally employ both arms in a dexterous manner, rot…
Human-Centered Development of Guide Dog Robots: Quiet and Stable Locomotion Control
Shangqun Yu, Hochul Hwang, Trung M. Dang +4
A quadruped robot is a promising system that can offer assistance comparable to that of dog guides due to its similar form factor. However, various challenges remain in making thes…
A Biomechanics-Inspired Approach to Soccer Kicking for Humanoid Robots
Daniel Marew, Nisal Perera, Shangqun Yu +2
Soccer kicking is a complex whole-body motion that requires intricate coordination of various motor actions. To accomplish such dynamic motion in a humanoid robot, the robot needs…
Learning Generic and Dynamic Locomotion of Humanoids Across Discrete Terrains
Shangqun Yu, Nisal Perera, Daniel Marew +1
This paper addresses the challenge of terrain-adaptive dynamic locomotion in humanoid robots, a problem traditionally tackled by optimization-based methods or reinforcement learnin…
Impedance Matching: Enabling an RL-Based Running Jump in a Quadruped Robot
Neil Guan, Shangqun Yu, Shifan Zhu +1
Replicating the remarkable athleticism seen in animals has long been a challenge in robotics control. Although Reinforcement Learning (RL) has demonstrated significant progress in…
StaccaToe: A Single-Leg Robot that Mimics the Human Leg and Toe
Nisal Perera, Shangqun Yu, Daniel Marew +6
We introduce StaccaToe, a human-scale, electric motor-powered single-leg robot designed to rival the agility of human locomotion through two distinctive attributes: an actuated toe…
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Megan M. Baker, Alexander New, Mario Aguilar-Simon +44
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks enc…
Model-based Lifelong Reinforcement Learning with Bayesian Exploration
Haotian Fu, Shangqun Yu, Michael Littman +1
We propose a model-based lifelong reinforcement-learning approach that estimates a hierarchical Bayesian posterior distilling the common structure shared across different tasks. Th…
Integration of Riemannian Motion Policy with Whole-Body Control for Collision-Free Legged Locomotion
Daniel Marew, Misha Lvovsky, Shangqun Yu +2
In this paper, we present a Riemannian Motion Policy (RMP)flow-based whole-body control framework for improved dynamic legged locomotion. RMPflow is a differential geometry-inspire…
Meta-Learning Parameterized Skills
Haotian Fu, Shangqun Yu, Saket Tiwari +2
We propose a novel parameterized skill-learning algorithm that aims to learn transferable parameterized skills and synthesize them into a new action space that supports efficient l…
Hierarchical Reinforcement Learning of Locomotion Policies in Response to Approaching Objects: A Preliminary Study
Shangqun Yu, Sreehari Rammohan, Kaiyu Zheng +1
Animals such as rabbits and birds can instantly generate locomotion behavior in reaction to a dynamic, approaching object, such as a person or a rock, despite having possibly never…
Learning Generalizable Behavior via Visual Rewrite Rules
Yiheng Xie, Mingxuan Li, Shangqun Yu +1
Though deep reinforcement learning agents have achieved unprecedented success in recent years, their learned policies can be brittle, failing to generalize to even slight modificat…
Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings
Sreehari Rammohan, Shangqun Yu, Bowen He +4
Learning continuous control in high-dimensional sparse reward settings, such as robotic manipulation, is a challenging problem due to the number of samples often required to obtain…