56 citations · 57 across the 21 of their papers we have counts for
12 papers · 1 filter
HiF-VLA: Hindsight, Insight and Foresight through Motion Representation for Vision-Language-Action Models
Minghui Lin, Pengxiang Ding, Shu Wang +7
Vision-Language-Action (VLA) models have recently enabled robotic manipulation by grounding visual and linguistic cues into actions. However, most VLAs assume the Markov property,…
Discovering Self-Protective Falling Policy for Humanoid Robot via Deep Reinforcement Learning
Diyuan Shi, Shangke Lyu, Donglin Wang
Humanoid robots have received significant research interests and advancements in recent years. Despite many successes, due to their morphology, dynamics and limitation of control p…
Dynamic Adaptive Legged Locomotion Policy via Decoupling Reaction Force Control and Gait Control
Renjie Wang, Shangke Lyu, Donglin Wang
While Reinforcement Learning (RL) has achieved remarkable progress in legged locomotion control, it often suffers from performance degradation in out-of-distribution (OOD) conditio…
Integrating Trajectory Optimization and Reinforcement Learning for Quadrupedal Jumping with Terrain-Adaptive Landing
Renjie Wang, Shangke Lyu, Xin Lang +2
Jumping constitutes an essential component of quadruped robots' locomotion capabilities, which includes dynamic take-off and adaptive landing. Existing quadrupedal jumping studies…
Robust Online Residual Refinement via Koopman-Guided Dynamics Modeling
Zhefei Gong, Shangke Lyu, Pengxiang Ding +2
Imitation learning (IL) enables efficient skill acquisition from demonstrations but often struggles with long-horizon tasks and high-precision control due to compounding errors. Re…
LAPO: Internalizing Reasoning Efficiency via Length-Adaptive Policy Optimization
Xingyu Wu, Yuchen Yan, Shangke Lyu +7
Large reasoning models have achieved remarkable performance through extended chain-of-thought sequences, yet this computational freedom leads to excessive token generation even for…
Hierarchical Budget Policy Optimization for Adaptive Reasoning
Shangke Lyu, Linjuan Wu, Yuchen Yan +7
Large reasoning models achieve remarkable performance through extensive chain-of-thought generation, yet they suffer from a critical inefficiency: applying uniformly extensive reas…
Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only
Wei Xiao, Jiacheng Liu, Zifeng Zhuang +3
Improving the performance of pre-trained policies through online reinforcement learning (RL) is a critical yet challenging topic. Existing online RL fine-tuning methods require con…
Learning Robotic Policy with Imagined Transition: Mitigating the Trade-off between Robustness and Optimality
Wei Xiao, Shangke Lyu, Zhefei Gong +2
Existing quadrupedal locomotion learning paradigms usually rely on extensive domain randomization to alleviate the sim2real gap and enhance robustness. It trains policies with a wi…
TDMPBC: Self-Imitative Reinforcement Learning for Humanoid Robot Control
Zifeng Zhuang, Diyuan Shi, Runze Suo +5
Complex high-dimensional spaces with high Degree-of-Freedom and complicated action spaces, such as humanoid robots equipped with dexterous hands, pose significant challenges for re…
GEVRM: Goal-Expressive Video Generation Model For Robust Visual Manipulation
Hongyin Zhang, Pengxiang Ding, Shangke Lyu +2
With the rapid development of embodied artificial intelligence, significant progress has been made in vision-language-action (VLA) models for general robot decision-making. However…
Activation-wise Propagation: A One-Timestep Strategy for Spiking Neural Networks
Jian Song, Xiangfei Yang, Shangke Lyu +1
Spiking neural networks (SNNs) have demonstrated significant potential in real-time multi-sensor perception tasks due to their event-driven and parameter-efficient characteristics.…