10 papers
ForceWAM: Decoupling Future and Response Learning from Demo and Play Data for Contact-Rich Manipulation
Zijun Xu, Wendi Chen, Xinkai Wang +16
Expert demonstrations show how to complete contact-rich tasks, but provide limited coverage of how interaction forces respond to different actions. Task-agnostic play provides broa…
ΔWAM: Distilling Action Tangent Fields into World Action Models
Ke Wu, Hanwen Huang, Bo Gu +6
World Action Models (WAM) improve robot policies by augmenting sparse action supervision with dense future prediction. However, much of the predictable future is dominated by appea…
CAP: Continuously Adaptive Perception-Blind Humanoid Locomotion via Learned Denoising
Hongjin Chen, Zijun Xu, Shihao Ma +8
Humanoid locomotion across complex terrain demands forward-looking exteroception to anticipate obstacles, yet this signal is unreliable in real-world deployment, failing partially…
HELP: Human-Efficient Large-Scale Robot Post-Training with Rollout Segmentation
Shaopeng Zhai, Qi Zhang, Tianyi Zhang +5
When adapting Vision Language Action (VLA) models to downstream tasks, multiple rounds of post-training are often required to progressively address policy weaknesses. In this repor…
CMoE: Contrastive Mixture of Experts for Motion Control and Terrain Adaptation of Humanoid Robots
Shihao Ma, Hongjin Chen, Zijun Xu +6
For effective deployment in real-world environments, humanoid robots must autonomously navigate a diverse range of complex terrains with abrupt transitions. While the Vanilla mixtu…
Rhythm: Learning Interactive Whole-Body Control for Dual Humanoids
Hongjin Chen, Wei Zhang, Pengfei Li +10
Realizing interactive whole-body control for multi-humanoid systems is critical for unlocking complex collaborative capabilities in shared environments. Although recent advancement…
Resolving State Ambiguity in Robot Manipulation via Adaptive Working Memory Recoding
Qingda Hu, Ziheng Qiu, Zijun Xu +7
State ambiguity is common in robotic manipulation. Identical observations may correspond to multiple valid behavior trajectories. The visuomotor policy must correctly extract the a…
Drive in Corridors: Enhancing the Safety of End-to-end Autonomous Driving via Corridor Learning and Planning
Zhiwei Zhang, Ruichen Yang, Ke Wu +5
Safety remains one of the most critical challenges in autonomous driving systems. In recent years, the end-to-end driving has shown great promise in advancing vehicle autonomy in a…
HGS-Planner: Hierarchical Planning Framework for Active Scene Reconstruction Using 3D Gaussian Splatting
Zijun Xu, Rui Jin, Ke Wu +6
In complex missions such as search and rescue,robots must make intelligent decisions in unknown environments, relying on their ability to perceive and understand their surroundings…
HGS-Mapping: Online Dense Mapping Using Hybrid Gaussian Representation in Urban Scenes
Ke Wu, Kaizhao Zhang, Zhiwei Zhang +7
Online dense mapping of urban scenes forms a fundamental cornerstone for scene understanding and navigation of autonomous vehicles. Recent advancements in mapping methods are mainl…