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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…
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…
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…
Look as You Leap: Planning Simultaneous Motion and Perception for High-DOF Robots
Qingxi Meng, Emiliano Flores, Carlos Quintero-Peña +5
Most common tasks for robots in dynamic spaces require that the environment is regularly and actively perceived. The perception task considered in this work can represent a broad r…
Falsification of Autonomous Systems in Rich Environments
Khen Elimelech, Morteza Lahijanian, Lydia E. Kavraki +1
Validating the behavior of autonomous Cyber-Physical Systems (CPS) and Artificial Intelligence (AI) agents, which rely on automated controllers, is an objective of great importance…
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…
Scaling Long-Horizon Online POMDP Planning via Rapid State Space Sampling
Yuanchu Liang, Edward Kim, Wil Thomason +3
Partially Observable Markov Decision Processes (POMDPs) are a general and principled framework for motion planning under uncertainty. Despite tremendous improvement in the scalabil…
Nearest-Neighbourless Asymptotically Optimal Motion Planning with Fully Connected Informed Trees (FCIT*)
Tyler S. Wilson, Wil Thomason, Zachary Kingston +2
Improving the performance of motion planning algorithms for high-degree-of-freedom robots usually requires reducing the cost or frequency of computationally expensive operations. T…
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…
Object-Centric Kinodynamic Planning for Nonprehensile Robot Rearrangement Manipulation
Kejia Ren, Gaotian Wang, Andrew S. Morgan +2
Nonprehensile actions such as pushing are crucial for addressing multi-object rearrangement problems. Many traditional methods generate robot-centric actions, which differ from int…
Encoding Reusable Multi-Robot Planning Strategies as Abstract Hypergraphs
Khen Elimelech, James Motes, Marco Morales +3
Multi-Robot Task Planning (MR-TP) is the search for a discrete-action plan a team of robots should take to complete a task. The complexity of such problems scales exponentially wit…
Task and Motion Planning for Execution in the Real
Tianyang Pan, Rahul Shome, Lydia E. Kavraki
Task and motion planning represents a powerful set of hybrid planning methods that combine reasoning over discrete task domains and continuous motion generation. Traditional reason…
Collision-Affording Point Trees: SIMD-Amenable Nearest Neighbors for Fast Collision Checking
Clayton W. Ramsey, Zachary Kingston, Wil Thomason +1
Motion planning against sensor data is often a critical bottleneck in real-time robot control. For sampling-based motion planners, which are effective for high-dimensional systems…
Stochastic Games for Interactive Manipulation Domains
Karan Muvvala, Andrew M. Wells, Morteza Lahijanian +2
As robots become more prevalent, the complexity of robot-robot, robot-human, and robot-environment interactions increases. In these interactions, a robot needs to consider not only…