12 papers · 1 filter
HiWE: Hierarchical World Knowledge Model with Visual Keypoint Enhancement for Zero-Shot 3D Path Planning
Guoqing Ma, Mingqi Yuan, Chen Gao +2
Robot demonstration generation requires a system to identify where an interaction should occur, plan a feasible motion, and execute the required contact. HiWE connects these decisi…
FAN: Foresight Action Normalization for Continual Adaptation of Vision-Language-Action Models
Yijun Hong, Jiarun Zhu, Xiaoquan Sun +6
Vision-Language-Action (VLA) models pre-trained on large-scale, closed datasets have demonstrated remarkable success across diverse robotic manipulation tasks. However, their long-…
Unified Condition-Action Modeling for Accurate One-Step Action Generation
Xinyu Zhou, Zikun Cai, Kuangji Zuo +7
Robot manipulation requires policies that are both accurate and efficient, as robot control must respond to changing observations under tight latency constraints. Recent diffusion…
GEM-Occ: From Visual Geometry Evidence to Embodied Semantic Occupancy Memory
Hu Zhu, Bohan Li, Xianda Guo +8
Embodied agents exploring indoor environments require reliable semantic occupancy memory that persists across observations and revisits. Building such memory is challenging because…
HiMem-WAM: Hierarchical Memory-Gated World Action Models for Robotic Manipulation
Xiaoquan Sun, Ruijian Zhang, Chen Cao +12
World Action Models (WAMs) have emerged as a new powerful paradigm for embodied intelligence, learning action-relevant visual dynamics that significantly enhance generalization and…
Can Vision-Language-Action Models Learn from Real-World Data Continually without Forgetting?
Jiarun Zhu, Yijun Hong, Xiaoquan Sun +7
Vision-Language-Action (VLA) models provide a promising foundation for general-purpose robotics, yet their real-world deployment demands the ability to continually acquire new skil…
AIM: Intent-Aware Unified world action Modeling with Spatial Value Maps
Liaoyuan Fan, Zetian Xu, Chen Cao +3
Pretrained video generation models provide strong priors for robot control, but existing unified world action models still struggle to decode reliable actions without substantial r…
AtomVLA: Scalable Post-Training for Robotic Manipulation via Predictive Latent World Models
Xiaoquan Sun, Zetian Xu, Chen Cao +9
Vision-Language-Action (VLA) models demonstrate remarkable potential for generalizable robotic manipulation. The execution of complex multi-step behaviors in VLA models can be impr…
Gait-Adaptive Perceptive Humanoid Locomotion with Real-Time Under-Base Terrain Reconstruction
Haolin Song, Hongbo Zhu, Tao Yu +5
For full-size humanoid robots, even with recent advances in reinforcement learning-based control, achieving reliable locomotion on complex terrains, such as long staircases, remain…
PvP: Data-Efficient Humanoid Robot Learning with Proprioceptive-Privileged Contrastive Representations
Mingqi Yuan, Tao Yu, Haolin Song +4
Achieving efficient and robust whole-body control (WBC) is essential for enabling humanoid robots to perform complex tasks in dynamic environments. Despite the success of reinforce…
A Survey of Behavior Foundation Model: Next-Generation Whole-Body Control System of Humanoid Robots
Mingqi Yuan, Tao Yu, Wenqi Ge +8
Humanoid robots are drawing significant attention as versatile platforms for complex motor control, human-robot interaction, and general-purpose physical intelligence. However, ach…
Hierarchical Procedural Framework for Low-latency Robot-Assisted Hand-Object Interaction
Mingqi Yuan, Huijiang Wang, Kai-Fung Chu +3
Advances in robotics have been driving the development of human-robot interaction (HRI) technologies. However, accurately perceiving human actions and achieving adaptive control re…