1 citations · 2 across the 16 of their papers we have counts for
18 papers
Interaction Latents as Actions for Contact-Rich Manipulation
Yuhang Zheng, Yujie Zang, Yuchen Liu +9
Contact-rich manipulation requires robots to coordinate not only where to move, but also how contact and force should evolve throughout an interaction. Yet most robot policies repr…
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…
GIFT: Guided Intermediate Feature Training via Action-Oriented Structural Supervision for Robotic Manipulation
Yupeng Zheng, Xiang Li, Songen Gu +11
Vision-language pre-training and predictive world modeling provide robot policies with rich semantic and dynamic visual features, but their native action and visual-prediction obje…
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…
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…
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…
-WM: A Unified Video-Action World Model for Robotic Manipulation
Pengfei Zhou, Shengcong Chen, Di Chen +17
Robotic manipulation requires models that generate executable actions while anticipating and evaluating their future consequences before physical execution. We present -World…
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…
OmniVTA: Visuo-Tactile World Modeling for Contact-Rich Robotic Manipulation
Yuhang Zheng, Songen Gu, Yupeng Zheng +11
Contact-rich manipulation tasks, such as wiping and assembly, require accurate perception of contact forces, friction changes, and state transitions that cannot be reliably inferre…
Mimir: Hierarchical Goal-Driven Diffusion with Uncertainty Propagation for End-to-End Autonomous Driving
Zebin Xing, Yupeng Zheng, Qichao Zhang +5
End-to-end autonomous driving has emerged as a pivotal direction in the field of autonomous systems. Recent works have demonstrated impressive performance by incorporating high-lev…
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…
UniArt: Unified 3D Representation for Generating 3D Articulated Objects with Open-Set Articulation
Bu Jin, Weize Li, Songen Gu +4
Articulated 3D objects play a vital role in realistic simulation and embodied robotics, yet manually constructing such assets remains costly and difficult to scale. In this paper,…
OccTENS: 3D Occupancy World Model via Temporal Next-Scale Prediction
Bu Jin, Songen Gu, Xiaotao Hu +5
In this paper, we propose OccTENS, a generative occupancy world model that enables controllable, high-fidelity long-term occupancy generation while maintaining computational effici…
Data Scaling Laws for Imitation Learning-Based End-to-End Autonomous Driving
Yupeng Zheng, Pengxuan Yang, Zhongpu Xia +9
The end-to-end autonomous driving paradigm has recently attracted lots of attention due to its scalability. However, existing methods are constrained by the limited scale of real-w…
DOME: Taming Diffusion Model into High-Fidelity Controllable Occupancy World Model
Songen Gu, Wei Yin, Bu Jin +5
We propose DOME, a diffusion-based world model that predicts future occupancy frames based on past occupancy observations. The ability of this world model to capture the evolution…
ComDrive: Comfort-Oriented End-to-End Autonomous Driving
Junming Wang, Xingyu Zhang, Zebin Xing +7
We propose ComDrive: the first comfort-oriented end-to-end autonomous driving system to generate temporally consistent and comfortable trajectories. Recent studies have demonstrate…
GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic Grasping
Yuhang Zheng, Xiangyu Chen, Yupeng Zheng +12
Constructing a 3D scene capable of accommodating open-ended language queries, is a pivotal pursuit, particularly within the domain of robotics. Such technology facilitates robots i…