2 citations · 2 across the 12 of their papers we have counts for
16 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…
AutoSpeed: Annotation-Free Stage-Adaptive Motion Speed Learning for Robot Manipulation
Qingda Hu, Ziheng Qiu, Jieru Zhao +2
Different stages of manipulation tasks exhibit varying levels of difficulty, suggesting stage-dependent motion speeds and temporal prediction horizons. However, existing IL-based v…
StreamKL: Fast and Memory-Efficient KL Divergence for Boosting Attention Distillation
Guangda Liu, Yiquan Wang, Chengwei Li +6
Attention distillation, which trains one attention distribution to match another by minimizing their Kullback-Leibler (KL) divergence, is widely used in knowledge distillation, mod…
Learning High-Frequency Continuous Action Chunks in Latent Space
Kunyun Wang, Yuhang Zheng, Yupeng Zheng +2
Modern robotic policies increasingly rely on action chunking to execute complex tasks in the physical world. While action chunking improves temporal consistency at moderate action…
Flash-Mono: Feed-Forward Accelerated Gaussian Splatting Monocular SLAM
Zicheng Zhang, Ke Wu, Xiangting Meng +3
Monocular 3D Gaussian Splatting SLAM suffers from critical limitations in time efficiency, geometric accuracy, and multi-view consistency. These issues stem from the time-consuming…
CoDAR: Continuous Diffusion Language Models are More Powerful Than You Think
Junzhe Shen, Jieru Zhao, Ziwei He +1
We study why continuous diffusion language models (DLMs) have lagged behind discrete diffusion approaches despite their appealing continuous generative dynamics. Under a controlled…
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…
World In Your Hands: A Large-Scale and Open-Source Ecosystem for Learning Human-Centric Manipulation in the Wild
Yupeng Zheng, Jichao Peng, Weize Li +22
We introduce World In Your Hands (WIYH), a large-scale open-source ecosystem comprising over 1,000 hours of human manipulation data collected in-the-wild with millimeter-scale moti…
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…
SparseTem: Boosting the Efficiency of CNN-Based Video Encoders by Exploiting Temporal Continuity
Kunyun Wang, Shuo Yang, Jieru Zhao +4
Deep learning models have become pivotal in the field of video processing and is increasingly critical in practical applications such as autonomous driving and object detection. Al…
AutoVCoder: A Systematic Framework for Automated Verilog Code Generation using LLMs
Mingzhe Gao, Jieru Zhao, Zhe Lin +5
Recently, the use of large language models (LLMs) for software code generation, e.g., C/C++ and Python, has proven a great success. However, LLMs still suffer from low syntactic an…
O2V-Mapping: Online Open-Vocabulary Mapping with Neural Implicit Representation
Muer Tie, Julong Wei, Zhengjun Wang +7
Online construction of open-ended language scenes is crucial for robotic applications, where open-vocabulary interactive scene understanding is required. Recently, neural implicit…
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…