21 papers
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…
EnergyBridge: Benchmarking Household Energy Management, User Participation, and Grid Flexibility
Xudong Wu, Zeqing Wu, Jiarui Zhang +8
Residential virtual power plants (VPPs) can provide grid flexibility by shifting household demand, but physical flexibility becomes dependable capacity only when residents authoriz…
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…
LargeMonitor: Monitoring Online Task-Free Continual Learning via Large Pretrained Models
Mingqi Yuan, Xiaoquan Sun, Shihao Luo +1
Online task-free continual learning (TFCL) requires intelligent agents to sequentially accumulate knowledge from an unbounded, non-stationary data stream under strict single-pass c…
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…
Vision Transformers that Never Stop Learning
Caihao Sun, Mingqi Yuan, Shiyuan Wang +1
Loss of plasticity refers to the progressive inability of a model to adapt to new tasks and poses a fundamental challenge for continual learning. While this phenomenon has been ext…
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…
Goal-Driven Reward by Video Diffusion Models for Reinforcement Learning
Qi Wang, Mian Wu, Yuyang Zhang +7
Reinforcement Learning (RL) has achieved remarkable success in various domains, yet it often relies on carefully designed programmatic reward functions to guide agent behavior. Des…
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…
Plasticine: Accelerating Research in Plasticity-Motivated Deep Reinforcement Learning
Mingqi Yuan, Qi Wang, Guozheng Ma +8
Developing lifelong learning agents is crucial for artificial general intelligence (AGI). However, deep reinforcement learning (RL) systems often suffer from plasticity loss, where…
ULTHO: Ultra-Lightweight yet Efficient Hyperparameter Optimization in Deep Reinforcement Learning
Mingqi Yuan, Bo Li, Xin Jin +1
Hyperparameter optimization (HPO) is a billion-dollar problem in machine learning, which significantly impacts the training efficiency and model performance. However, achieving eff…
Deep Reinforcement Learning with Hybrid Intrinsic Reward Model
Mingqi Yuan, Bo Li, Xin Jin +1
Intrinsic reward shaping has emerged as a prevalent approach to solving hard-exploration and sparse-rewards environments in reinforcement learning (RL). While single intrinsic rewa…
Adaptive Data Exploitation in Deep Reinforcement Learning
Mingqi Yuan, Bo Li, Xin Jin +1
We introduce ADEPT: Adaptive Data ExPloiTation, a simple yet powerful framework to enhance the **data efficiency** and **generalization** in deep reinforcement learning (RL). Speci…
RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning
Mingqi Yuan, Roger Creus Castanyer, Bo Li +3
Extrinsic rewards can effectively guide reinforcement learning (RL) agents in specific tasks. However, extrinsic rewards frequently fall short in complex environments due to the si…
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…