18 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…
Contact Trajectory Prompting: In-Context Transfer of Contact-Rich Behaviors from a Single Demonstration
Xian Nie, Yujie Zang, Yuhang Zheng +8
In-context robot learning enables robots to acquire new behaviors from demonstrations without test-time parameter updates. However, visual and kinematic specifications alone may no…
TacDyn-WAM: Learning Implicit Tactile Dynamics in a Heterogeneous Visuo-Tactile World Action Model
Enyi Wang, Mingxin Wang, Quan Shi +12
World action models improve robotic manipulation by conditioning actions on predicted futures, yet existing tactile variants largely inherit video-generation pipelines that reconst…
Latent Action as Intention Enables Efficient Future Imagination for World Action Models
Xiang Li, Yupeng Zheng, Songen Gu +11
World action models (WAMs) improve robot control by modeling how observations evolve, but generating future observations at test time incurs substantial latency. Fast-WAM removes t…
StageWAM: Joint-Embedding Stage Prediction for World-Action Models in Robot Manipulation
Xiao Liu, Yuguang Yang, Xi Wang +6
Generalist robot policies aim to map multimodal observations and linguistic task instructions to actions across diverse tasks. However, existing methods typically represent the fut…
VT-WAM: Visual-Tactile World Action Model for Contact-Rich Manipulation
Shuai Tian, Yupeng Zheng, Yuhang Zheng +7
Contact-rich manipulation requires policies to react to local deformation, pressure, slip, and friction, yet these cues are temporally sparse and often invisible in visual observat…
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…
TacForeSight: Force-Guided Tactile World Model for Contact-Rich Manipulation
Yujie Zang, Yuhang Zheng, Xian Nie +7
Contact-rich manipulation requires robots to continuously perceive and regulate evolving physical interactions under dynamic contact transitions or complex surface geometries. Rece…
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…
PokeVLA: Empowering Pocket-Sized Vision-Language-Action Model with Comprehensive World Knowledge Guidance
Yupeng Zheng, Xiang Li, Songen Gu +12
Recent advances in Vision-Language-Action (VLA) models have opened new avenues for robot manipulation, yet existing methods exhibit limited efficiency and a lack of high-level know…
VistaBot: View-Robust Robot Manipulation via Spatiotemporal-Aware View Synthesis
Songen Gu, Yuhang Zheng, Weize Li +6
Recently, end-to-end robotic manipulation models have gained significant attention for their generalizability and scalability. However, they often suffer from limited robustness to…
Enhancing Indoor Occupancy Prediction via Sparse Query-Based Multi-Level Consistent Knowledge Distillation
Xiang Li, Yupeng Zheng, Pengfei Li +3
Occupancy prediction provides critical geometric and semantic understanding for robotics but faces efficiency-accuracy trade-offs. Current dense methods suffer computational waste…
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…
Guiding the Inner Eye: A Framework for Hierarchical and Flexible Visual Grounded Reasoning
Zhaoyang Wei, Wenchao Ding, Yanchao Hao +1
Models capable of "thinking with images" by dynamically grounding their reasoning in visual evidence represent a major leap in multimodal AI. However, replicating and advancing thi…
STREAMINGGS: Voxel-Based Streaming 3D Gaussian Splatting with Memory Optimization and Architectural Support
Chenqi Zhang, Yu Feng, Jieru Zhao +4
3D Gaussian Splatting (3DGS) has gained popularity for its efficiency and sparse Gaussian-based representation. However, 3DGS struggles to meet the real-time requirement of 90 fram…
LiveVLM: Efficient Online Video Understanding via Streaming-Oriented KV Cache and Retrieval
Zhenyu Ning, Guangda Liu, Qihao Jin +4
Recent developments in Video Large Language Models (Video LLMs) have enabled models to process hour-long videos and exhibit exceptional performance. Nonetheless, the Key-Value (KV)…
Dual-AEB: Synergizing Rule-Based and Multimodal Large Language Models for Effective Emergency Braking
Wei Zhang, Pengfei Li, Junli Wang +8
Automatic Emergency Braking (AEB) systems are a crucial component in ensuring the safety of passengers in autonomous vehicles. Conventional AEB systems primarily rely on closed-set…