26 papers
TS-WAM: Temporal Latent Actions and World-Action Coupling for Vision-Language Robot Control
Menghao Yin, Jiaju Yin, Xiyue Dong +5
Many vision-language-action policies rely on action-labeled robot demonstrations and lack an explicit mechanism for predicting future visual states. These limitations hinder learni…
MM-ABC: Towards Generalist Mobile Manipulation via Seeing, Coordinating and Imagining
Qiwei Liang, Guangyu Chen, Shaolong Zhu +6
Mobile manipulation extends robot interaction beyond a fixed kinematic workspace by making the reachable region itself controllable. This flexibility introduces two central challen…
What is the Better Curriculum: Controller-Shaped Grasping Behavior for Contact Force-Sensitive Manipulation
Ziyan Feng, Zizhao Yuan, Yulong Fu +6
How should a robot learn to manipulate objects so fragile that sub-Newton contact forces can cause irreversible damage? Existing visuo-tactile policy learning typically treats tact…
Air-Ground Collaborative Vision-and-Language Navigation via Shared Bird's-Eye Maps
Shuning Zhang, Liang Li, Yunheng Wang +3
Air-ground collaborative Vision-and-Language Navigation (VLN) pairs an unmanned aerial vehicle (UAV) with a global bird's-eye view and an unmanned ground vehicle (UGV) with a local…
How to Learn from What a Human Would Avoid? Intervention-Aware World Models with Real-World RL for Dexterous Manipulation
Jiaju Yin, Zhenhui Zhang, Lixin Xu +5
Multi-fingered dexterous manipulation remains a frontier for real-world reinforcement learning (RL) due to the high-dimensional action space and the prohibitive cost of hardware fa…
DreamTrajectory: Trajectory-Guided Action Generation with World Model Alignment for Mobile Manipulation
Zheng Yang, Wenjie Zhang, Xiangyu Chen +9
Mobile manipulation requires a robot to coordinate base and arm motion under continuously changing viewpoints and contact conditions, within an action space far larger than that of…
Open-AoE: An Open Egocentric Manipulation Dataset and Toolchain for Embodied Learning
Zishuo Li, Bowen Yang, Changtao Miao +29
Egocentric videos of human manipulation provide scalable supervision for embodied intelligence, yet existing resources rarely combine low-cost continuous capture, manipulation-leve…
A Neuromorphic Reinforcement Learning Framework for Efficient Pathfinding in Robotic Mobile Fulfillment Systems
Junzhe Xu, Zecui Zeng, Lusong Li +2
Dynamic environmental changes, confined workspaces, and stringent real-time constraints make pathfinding in Robotic Mobile Fulfillment Systems (RMFS) a challenging problem for conv…
PL-LIT: A LiDAR-Inertial-Thermal SLAM Using Point-Line Features and Thermographic Mapping
Jiawei Xia, Yixiao Feng, Yongliang Shi +4
Thermal imaging is resilient to adverse conditions, such as intense illumination, low-light operation, and fog, and can therefore mitigate odometry degradation when visible-spectru…
Not All Actions Are Equal: Rethinking Conditioning for Dexterous World Model
Zizhao Yuan, Zhengtu Liang, Taowen Wang +7
Recent advances in action-conditioned world models show promising progress in modeling complex interactions and forecasting future states under diverse action sequences. While thes…
HumanoidArena: Benchmarking Egocentric Hierarchical Whole-body Learning
Taowen Wang, Zikang Xie, Bin Yang +13
Humanoid robots promise whole-body interaction in human-centered environments, but scalable policy learning remains difficult because task-level decision-making and whole-body dyna…
CoRe-MoE: Contrastive Reweighted Mixture of Experts for Multi-Terrain Humanoid Locomotion with Gait Adaptation
Kailun Huang, Zikang Xie, Yanzhe Xie +8
Humans primarily rely on walking and running to traverse complex terrains. Similarly, humanoid robots should be able to smoothly transition between walking and running while mainta…
ParkourFormer: Integrating Predictive Supervision and Sequence Modeling into Parkour Locomotion
Yanheng Mai, Wenhao Xu, Zirui Huang +6
Humanoid parkour requires locomotion policies to coordinate whole-body dynamics across rapidly changing terrains such as stairs, gaps, slopes, and obstacles. Existing learning-base…
Action-Effect Memory Pretraining for Robot Manipulation
Yijing Zhou, Qiwei Liang, Sitong Zhuang +5
We present AEM, an Action-Effect Memory pretraining framework for robot manipulation that learns compact temporal representations from vision-action history. Unlike prior robot rep…
Tabero: Learning Gentle Manipulation with Closed-Loop Force Feedback from Vision, Touch, and Language
Qiwei Wu, Rui Zhang, Xin Xiang +4
Tactile sensing is essential for robots to achieve human-like gentle manipulation. However, existing Vision-Language-Action (VLA) models struggle to exploit tactile feedback for ge…
OHP-RL: Online Human Preference as Guidance in Reinforcement Learning for Robot Manipulation
Yunyang Mo, Jian Li, Qiwei Wu +2
While reinforcement learning (RL) enables robots to acquire skills autonomously, its real-world deployment is severely limited by inefficient and unsafe exploration. Human-in-the-l…
What Limits Vision-and-Language Navigation ?
Yunheng Wang, Yuetong Fang, Taowen Wang +9
Vision-and-Language Navigation (VLN) is a cornerstone of embodied intelligence. However, current agents often suffer from significant performance degradation when transitioning fro…
Morphology-Consistent Humanoid Interaction through Robot-Centric Video Synthesis
Weisheng Xu, Jian Li, Yi Gu +12
Equipping humanoid robots with versatile interaction skills typically requires either extensive policy training or explicit human-to-robot motion retargeting. However, learning-bas…
Beyond Viewpoint Generalization: What Multi-View Demonstrations Offer and How to Synthesize Them for Robot Manipulation?
Boyang Cai, Qiwei Liang, Jiawei Li +11
Does multi-view demonstration truly improve robot manipulation, or merely enhance cross-view robustness? We present a systematic study quantifying the performance gains, scaling be…
UniGround: Universal 3D Visual Grounding via Training-Free Scene Parsing
Jiaxi Zhang, Yunheng Wang, Wei Lu +8
3D Visual Grounding (3DVG) localizes objects from natural-language descriptions in 3D scenes and is fundamental to embodied AI applications. Although foundation models enable open-…
T2MBench: A Benchmark for Out-of-Distribution Text-to-Motion Generation
Bin Yang, Rong Ou, Weisheng Xu +7
Most existing evaluations of text-to-motion generation focus on in-distribution textual inputs and a limited set of evaluation criteria, which restricts their ability to systematic…
On-the-Fly VLA Adaptation via Test-Time Reinforcement Learning
Changyu Liu, Yiyang Liu, Taowen Wang +7
Vision-Language-Action models have recently emerged as a powerful paradigm for general-purpose robot learning, enabling agents to map visual observations and natural-language instr…
HERO: Hierarchical Traversable 3D Scene Graphs for Embodied Navigation Among Movable Obstacles
Yunheng Wang, Yixiao Feng, Yuetong Fang +5
3D Scene Graphs (3DSGs) constitute a powerful representation of the physical world, distinguished by their abilities to explicitly model the complex spatial, semantic, and function…
Gentle Manipulation Policy Learning via Demonstrations from VLM Planned Atomic Skills
Jiayu Zhou, Qiwei Wu, Jian Li +3
Autonomous execution of long-horizon, contact-rich manipulation tasks traditionally requires extensive real-world data and expert engineering, posing significant cost and scalabili…