5 citations · 5 across the 6 of their papers we have counts for
8 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…
KineWorld: Action-Induced Transport Fields for Embodied World Modeling
Ziying Song, Yuchen Liu, Zhuoran Xu +6
Embodied world models predict the visual consequences of candidate actions before execution. However, existing action-conditioned world models often adopt uniformly weighted visual…
Not All History Helps: Velocity-Aware Selective Memory for Long-Horizon End-to-End Autonomous Driving
Yuchen Liu, Ziying Song, Shengkai Zhang +6
Reliable long-horizon planning remains a key challenge in end-to-end autonomous driving. By accounting for future motion evolution and potential consequences, it provides forward-l…
Agentic Open RAN: A Deterministic and Auditable Framework for Intent-Driven Radio Control
Hengxu Li, Dongkuan Xu, Mingzhe Chen +1
Large language models (LLMs) open new possibilities for agentic control in Open RAN, allowing operators to express intents in natural language while delegating low-level execution…
Weakly-supervised Learning for Physics-informed Neural Motion Planning via Sparse Roadmap
Ruiqi Ni, Yuchen Liu, Ahmed H. Qureshi
The motion planning problem requires finding a collision-free path between start and goal configurations in high-dimensional, cluttered spaces. Recent learning-based methods offer…
Online Hierarchical Policy Learning using Physics Priors for Robot Navigation in Unknown Environments
Wei Han Chen, Yuchen Liu, Alexiy Buynitsky +1
Robot navigation in large, complex, and unknown indoor environments is a challenging problem. The existing approaches, such as traditional sampling-based methods, struggle with res…
Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments
Yuchen Liu, Alexiy Buynitsky, Ruiqi Ni +1
Physics-informed Neural Motion Planners (PiNMPs) provide a data-efficient framework for solving the Eikonal Partial Differential Equation (PDE) and representing the cost-to-go func…
Physics-informed Neural Mapping and Motion Planning in Unknown Environments
Yuchen Liu, Ruiqi Ni, Ahmed H. Qureshi
Mapping and motion planning are two essential elements of robot intelligence that are interdependent in generating environment maps and navigating around obstacles. The existing ma…