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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…
Zero-Shot Reactive Obstacle Avoidance for Generative Robot Policies
Weihang Guo, Lydia E. Kavraki
We propose NUDGE (Nudge Update via Differentiable GEometry), a training-free obstacle-avoidance procedure that can be incorporated in any robot policy based on diffusion or flow ma…
Fibration Trees: A Unified Approach to Multi-Robot Motion Planning
Andreas Orthey, Florian T. Pokorny, Lydia E. Kavraki
State space projections and decompositions have emerged as powerful tools to tackle the curse of dimensionality in high-dimensional, multi-robot motion planning problems. However,…
Think Fast and Far: Long-Horizon Online POMDP Planning via Rapid State Sampling
Yuanchu Liang, Edward Kim, J. Arden Knoll +4
Partially Observable Markov Decision Processes (POMDPs) are a general and principled framework for motion planning under uncertainty. Despite tremendous improvement in the scalabil…
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…
Sampling-Based Motion Planning with Scene Graphs Under Perception Constraints
Qingxi Meng, Emiliano Flores, Thai Duong +2
It will be increasingly common for robots to operate in cluttered human-centered environments such as homes, workplaces, and hospitals, where the robot is often tasked to maintain…
Ultrafast Sampling-based Kinodynamic Planning via Differential Flatness
Thai Duong, Clayton W. Ramsey, Zachary Kingston +2
Motion planning under dynamics constraints, i.e, kinodynamic planning, enables safe robot operation by generating dynamically feasible trajectories that the robot can accurately tr…
TIDE: A Trace-Informed Depth-First Exploration for Planning with Temporally Extended Goals
Yuliia Suprun, Khen Elimelech, Lydia E. Kavraki +1
Task planning with temporally extended goals (TEGs) is a critical challenge in AI and robotics, enabling agents to achieve complex sequences of objectives over time rather than add…