36 citations · 55 across the 14 of their papers we have counts for
15 papers
Continual Quadruped Robots Coordination via Semantic Skill Discovery
Daoqing Wang, Yuchen Xiao, Weixuan Huang +5
Multi-quadruped coordination has attracted increasing attention due to its enhanced payload capacity, broader contact coverage, and improved adaptability to challenging tasks. Exis…
OmniXtreme: Breaking the Generality Barrier in High-Dynamic Humanoid Control
Yunshen Wang, Shaohang Zhu, Peiyuan Zhi +7
High-fidelity motion tracking serves as the ultimate litmus test for generalizable, human-level motor skills. However, current policies often hit a "generality barrier": as motion…
On Centralized Critics in Multi-Agent Reinforcement Learning
Xueguang Lyu, Andrea Baisero, Yuchen Xiao +2
Centralized Training for Decentralized Execution where agents are trained offline in a centralized fashion and execute online in a decentralized manner, has become a popular approa…
On-Robot Bayesian Reinforcement Learning for POMDPs
Hai Nguyen, Sammie Katt, Yuchen Xiao +1
Robot learning is often difficult due to the expense of gathering data. The need for large amounts of data can, and should, be tackled with effective algorithms and leveraging expe…
Sequential Fair Resource Allocation under a Markov Decision Process Framework
Parisa Hassanzadeh, Eleonora Kreacic, Sihan Zeng +2
We study the sequential decision-making problem of allocating a limited resource to agents that reveal their stochastic demands on arrival over a finite horizon. Our goal is to des…
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning
Yuchen Xiao, Weihao Tan, Christopher Amato
Synchronizing decisions across multiple agents in realistic settings is problematic since it requires agents to wait for other agents to terminate and communicate about termination…
Macro-Action-Based Multi-Agent/Robot Deep Reinforcement Learning under Partial Observability
Yuchen Xiao
The state-of-the-art multi-agent reinforcement learning (MARL) methods have provided promising solutions to a variety of complex problems. Yet, these methods all assume that agents…
A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning
Xueguang Lyu, Andrea Baisero, Yuchen Xiao +1
Centralized Training for Decentralized Execution, where training is done in a centralized offline fashion, has become a popular solution paradigm in Multi-Agent Reinforcement Learn…
Local Advantage Actor-Critic for Robust Multi-Agent Deep Reinforcement Learning
Yuchen Xiao, Xueguang Lyu, Christopher Amato
Policy gradient methods have become popular in multi-agent reinforcement learning, but they suffer from high variance due to the presence of environmental stochasticity and explori…
Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning
Xueguang Lyu, Yuchen Xiao, Brett Daley +1
Centralized Training for Decentralized Execution, where agents are trained offline using centralized information but execute in a decentralized manner online, has gained popularity…
Adaptive directional Haar tight framelets on bounded domains for digraph signal representations
Yuchen Xiao, Xiaosheng Zhuang
Based on hierarchical partitions, we provide the construction of Haar-type tight framelets on any compact set . In particular, on the unit block ,…
Macro-Action-Based Deep Multi-Agent Reinforcement Learning
Yuchen Xiao, Joshua Hoffman, Christopher Amato
In real-world multi-robot systems, performing high-quality, collaborative behaviors requires robots to asynchronously reason about high-level action selection at varying time durat…
Learning Multi-Robot Decentralized Macro-Action-Based Policies via a Centralized Q-Net
Yuchen Xiao, Joshua Hoffman, Tian Xia +1
In many real-world multi-robot tasks, high-quality solutions often require a team of robots to perform asynchronous actions under decentralized control. Decentralized multi-agent r…
Numerical construction of spherical -designs by Barzilai-Borwein method
Yuchen Xiao, Congpei An
A point set on the unit sphere is a spherical -design is equivalent to the nonnegative quantity vanished. We show that if is a stationary…
Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems
Trong Nghia Hoang, Yuchen Xiao, Kavinayan Sivakumar +2
A key challenge in multi-robot and multi-agent systems is generating solutions that are robust to other self-interested or even adversarial parties who actively try to prevent the…