56 citations · 57 across the 21 of their papers we have counts for
17 papers · 1 filter
World-Value-Action Model: Implicit Planning for Vision-Language-Action Systems
Runze Li, Hongyin Zhang, Junxi Jin +5
Vision-Language-Action (VLA) models have emerged as a promising paradigm for building embodied agents that ground perception and language into action. However, most existing approa…
VAMPO: Policy Optimization for Improving Visual Dynamics in Video Action Models
Zirui Ge, Pengxiang Ding, Baohua Yin +16
Video action models are an appealing foundation for Vision--Language--Action systems because they can learn visual dynamics from large-scale video data and transfer this knowledge…
VORL-EXPLORE: A Hybrid Learning Planning Approach to Multi-Robot Exploration in Dynamic Environments
Ning Liu, Sen Shen, Zheng Li +5
Hierarchical multi-robot exploration decouples frontier allocation from local navigation, making systems brittle in dynamic environments. Without awareness of execution difficulty,…
CMR: Contractive Mapping Embeddings for Robust Humanoid Locomotion on Unstructured Terrains
Qixin Zeng, Hongyin Zhang, Shangke Lyu +3
Robust disturbance rejection remains a longstanding challenge in humanoid locomotion, particularly on unstructured terrains where sensing is unreliable and model mismatch is pronou…
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…
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…
QUART-Online: Latency-Free Large Multimodal Language Model for Quadruped Robot Learning
Xinyang Tong, Pengxiang Ding, Yiguo Fan +9
This paper addresses the inherent inference latency challenges associated with deploying multimodal large language models (MLLM) in quadruped vision-language-action (QUAR-VLA) task…
CARP: Visuomotor Policy Learning via Coarse-to-Fine Autoregressive Prediction
Zhefei Gong, Pengxiang Ding, Shangke Lyu +5
In robotic visuomotor policy learning, diffusion-based models have achieved significant success in improving the accuracy of action trajectory generation compared to traditional au…
GeRM: A Generalist Robotic Model with Mixture-of-experts for Quadruped Robot
Wenxuan Song, Han Zhao, Pengxiang Ding +4
Multi-task robot learning holds significant importance in tackling diverse and complex scenarios. However, current approaches are hindered by performance issues and difficulties in…
QUAR-VLA: Vision-Language-Action Model for Quadruped Robots
Pengxiang Ding, Han Zhao, Wenjie Zhang +5
The important manifestation of robot intelligence is the ability to naturally interact and autonomously make decisions. Traditional approaches to robot control often compartmentali…
Unlock Reliable Skill Inference for Quadruped Adaptive Behavior by Skill Graph
Hongyin Zhang, Diyuan Shi, Zifeng Zhuang +6
Developing robotic intelligent systems that can adapt quickly to unseen wild situations is one of the critical challenges in pursuing autonomous robotics. Although some impressive…