8 papers
Foundation-Model-Guided Topology-Aware Semantic Risk Fields for Manipulation
Giung Lee, Weihang Guo, Lydia E. Kavraki
Robot motion planning in everyday environments must satisfy hard geometric constraints while accounting for context-dependent semantic risk. We present a foundation-model-guided, t…
Scaling Video Generation for Reasoning: At What Cost?
Weihang Guo, Xiaoyu Wu, Yifei Wang +2
We study whether scaling video generation enables models to reason about hidden information from the past frames, and at what computational cost. Our controlled benchmark requires…
Compress to Remember: Learning Compact Memory via On-Policy Distillation for Long Video Generation
Xiaoyu Wu, Weihang Guo, Yifei Wang +3
Standard video generators do not natively compact historical context into reusable memory tokens. As generation continues, the growing history makes it increasingly difficult to re…
The Open Motion Planning Library 2.0
Weihang Guo, Theodoros Tyrovouzis, Emiliano Flores +5
The Open Motion Planning Library (OMPL), first released in 2008, has become a cornerstone of the motion planning community, providing implementations of a wide range of state-of-th…
Python Bindings for a Large C++ Robotics Library: The Case of OMPL
Weihang Guo, Theodoros Tyrovouzis, Lydia E. Kavraki
Python bindings are a critical bridge between high-performance C++ libraries and the flexibility of Python, enabling rapid prototyping, reproducible experiments, and integration wi…
Using VLM Reasoning to Constrain Task and Motion Planning
Muyang Yan, Miras Mengdibayev, Ardon Floros +3
In task and motion planning, high-level task planning is done over an abstraction of the world to enable efficient search in long-horizon robotics problems. However, the feasibilit…
Efficient Multi-Robot Motion Planning for Manifold-Constrained Manipulators by Randomized Scheduling and Informed Path Generation
Weihang Guo, Zachary Kingston, Kaiyu Hang +1
Multi-robot motion planning for high degree-of-freedom manipulators in shared, constrained, and narrow spaces is a complex problem and essential for many scenarios such as construc…
CaStL: Constraints as Specifications through LLM Translation for Long-Horizon Task and Motion Planning
Weihang Guo, Zachary Kingston, Lydia E. Kavraki
Large Language Models (LLMs) have demonstrated remarkable ability in long-horizon Task and Motion Planning (TAMP) by translating clear and straightforward natural language problems…