Publications (13)
Gallant: Voxel Grid-based Humanoid Locomotion and Local-navigation across 3D Constrained Terrains
Qingwei Ben, Botian Xu, Kailin Li +6
Robust humanoid locomotion requires accurate and globally consistent perception of the surrounding 3D environment. However, existing perception modules, mainly based on depth image…
Context-Aware Aerial Object Detection: Leveraging Inter-Object and Background Relationships
Botao Ren, Botian Xu, Xue Yang +3
In most modern object detection pipelines, the detection proposals are processed independently given the feature map. Therefore, they overlook the underlying relationships between…
FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots
Botian Xu, Haoyang Weng, Qingzhou Lu +2
Reinforcement learning (RL) has made significant strides in legged robot control, enabling locomotion across diverse terrains and complex loco-manipulation capabilities. However, t…
MoE-DP: An MoE-Enhanced Diffusion Policy for Robust Long-Horizon Robotic Manipulation with Skill Decomposition and Failure Recovery
Baiye Cheng, Tianhai Liang, Suning Huang +5
Diffusion policies have emerged as a powerful framework for robotic visuomotor control, yet they often lack the robustness to recover from subtask failures in long-horizon, multi-s…
Multi-UAV Formation Control with Static and Dynamic Obstacle Avoidance via Reinforcement Learning
Yuqing Xie, Chao Yu, Hongzhi Zang +7
This paper tackles the challenging task of maintaining formation among multiple unmanned aerial vehicles (UAVs) while avoiding both static and dynamic obstacles during directed fli…
Feedback RoI Features Improve Aerial Object Detection
Botao Ren, Botian Xu, Tengyu Liu +2
Neuroscience studies have shown that the human visual system utilizes high-level feedback information to guide lower-level perception, enabling adaptation to signals of different c…
OmniDrones: An Efficient and Flexible Platform for Reinforcement Learning in Drone Control
Botian Xu, Feng Gao, Chao Yu +3
In this work, we introduce OmniDrones, an efficient and flexible platform tailored for reinforcement learning in drone control, built on Nvidia's Omniverse Isaac Sim. It employs a…
A Dual Curriculum Learning Framework for Multi-UAV Pursuit-Evasion in Diverse Environments
Jiayu Chen, Guosheng Li, Chao Yu +4
This paper addresses multi-UAV pursuit-evasion, where a group of drones cooperates to capture a fast evader in a confined environment with obstacles. Existing heuristic algorithms,…
Online Planning for Multi-UAV Pursuit-Evasion in Unknown Environments Using Deep Reinforcement Learning
Jiayu Chen, Chao Yu, Guosheng Li +6
Multi-UAV pursuit-evasion, where pursuers aim to capture evaders, poses a key challenge for UAV swarm intelligence. Multi-agent reinforcement learning (MARL) has demonstrated poten…
Lamarckian Platform: Pushing the Boundaries of Evolutionary Reinforcement Learning towards Asynchronous Commercial Games
Hui Bai, Ruimin Shen, Yue Lin +2
Despite the emerging progress of integrating evolutionary computation into reinforcement learning, the absence of a high-performance platform endowing composability and massive par…
ArtFormer: Controllable Generation of Diverse 3D Articulated Objects
Jiayi Su, Youhe Feng, Zheng Li +4
This paper presents a novel framework for modeling and conditional generation of 3D articulated objects. Troubled by flexibility-quality tradeoffs, existing methods are often limit…
On the Evaluation of Generative Robotic Simulations
Feng Chen, Botian Xu, Pu Hua +4
Due to the difficulty of acquiring extensive real-world data, robot simulation has become crucial for parallel training and sim-to-real transfer, highlighting the importance of sca…
Learning Zero-Shot Cooperation with Humans, Assuming Humans Are Biased
Chao Yu, Jiaxuan Gao, Weilin Liu +5
There is a recent trend of applying multi-agent reinforcement learning (MARL) to train an agent that can cooperate with humans in a zero-shot fashion without using any human data.…