1 citations · 1 across the 15 of their papers we have counts for
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STEAM: Self-Supervised Temporal Ensemble Advantage Modeling for Real-World Robot Learning
Zhihao Liu, Qiuyi Gu, Yitao Wang +16
Real-world robot learning increasingly relies on heterogeneous data, but demonstrations and rollouts often mix useful progress with stalls, corrections, and suboptimal behavior. Ef…
LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies
Jialei Chen, Kai Wang, Kang Chen +9
Vision-Language-Action models (VLAs) leverage large-scale vision-language pretraining for semantic robot control, but often lack explicit foresight into how robot actions change th…
StreamingVLA: Streaming Vision-Language-Action Model with Action Flow Matching and Adaptive Early Observation
Yiran Shi, Dongqi Guo, Tianchen Zhao +8
Vision-language-action (VLA) models have demonstrated exceptional performance in natural language-driven perception and control. However, the high computational cost of VLA models…
Beyond Imitation: Reinforcement Learning-Based Sim-Real Co-Training for VLA Models
Liangzhi Shi, Shuaihang Chen, Feng Gao +8
Simulation offers a scalable and low-cost way to enrich vision-language-action (VLA) training, reducing reliance on expensive real-robot demonstrations. However, most sim-real co-t…
USER: A Unified and Extensible System for Real-World Online Policy Learning in Embodied AI
Hongzhi Zang, Shu'ang Yu, Hao Lin +14
Online policy learning directly in the physical world is a promising yet challenging direction for embodied intelligence. Unlike simulation, real-world systems cannot be arbitraril…
D3P: Dynamic Denoising Diffusion Policy via Reinforcement Learning
Shu-Ang Yu, Feng Gao, Yi Wu +2
Diffusion policies excel at learning complex action distributions for robotic visuomotor tasks, yet their iterative denoising process poses a major bottleneck for real-time deploym…
Hysteresis-Aware Neural Network Modeling and Whole-Body Reinforcement Learning Control of Soft Robots
Zongyuan Chen, Yan Xia, Jiayuan Liu +10
Soft robots exhibit inherent compliance and safety, which makes them particularly suitable for applications requiring direct physical interaction with humans, such as surgical proc…
Multi-Robot System for Cooperative Exploration in Unknown Environments: A Survey
Chuqi Wang, Chao Yu, Xin Xu +11
With the real need of field exploration in large-scale and extreme outdoor environments, cooperative exploration tasks have garnered increasing attention. This paper presents a com…
What Matters in Learning A Zero-Shot Sim-to-Real RL Policy for Quadrotor Control? A Comprehensive Study
Jiayu Chen, Chao Yu, Yuqing Xie +9
Executing precise and agile flight maneuvers is critical for quadrotors in various applications. Traditional quadrotor control approaches are limited by their reliance on flat traj…
Neural Internal Model Control: Learning a Robust Control Policy via Predictive Error Feedback
Feng Gao, Chao Yu, Yu Wang +1
Accurate motion control in the face of disturbances within complex environments remains a major challenge in robotics. Classical model-based approaches often struggle with nonlinea…
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…
FlightBench: Benchmarking Learning-based Methods for Ego-vision-based Quadrotors Navigation
Shu-Ang Yu, Chao Yu, Feng Gao +2
Ego-vision-based navigation in cluttered environments is crucial for mobile systems, particularly agile quadrotors. While learning-based methods have shown promise recently, head-t…