11 papers
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…
RLinf-VLA: A Unified and Efficient Framework for Reinforcement Learning of Vision-Language-Action Models
Hongzhi Zang, Mingjie Wei, Si Xu +15
Recent studies have demonstrated the potential of reinforcement learning (RL) to improve the task performance of vision-language-action (VLA) models through interaction. However, c…
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…
Spec-VLA: Speculative Decoding for Vision-Language-Action Models with Relaxed Acceptance
Songsheng Wang, Rucheng Yu, Zhihang Yuan +4
Vision-Language-Action (VLA) models have made substantial progress by leveraging the robust capabilities of Visual Language Models (VLMs). However, VLMs' significant parameter size…
What Can RL Bring to VLA Generalization? An Empirical Study
Jijia Liu, Feng Gao, Bingwen Wei +5
Large Vision-Language Action (VLA) models have shown significant potential for embodied AI. However, their predominant training via supervised fine-tuning (SFT) limits generalizati…
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…
Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network
Jijia Liu, Feng Gao, Qingmin Liao +2
Reinforcement learning (RL) for continuous control often requires large amounts of online interaction data. Value-based RL methods can mitigate this burden by offering relatively h…
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…