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Collision-Affording Point Trees: SIMD-Amenable Nearest Neighbors for Fast Motion Planning with Pointclouds

https://doi.org/10.15607/RSS.2024.XX.038

Ramsey, Clayton; Kingston, Zachary; Thomason, Wil; Kavraki, Lydia (July 2024, Robotics: Science and Systems Foundation)

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Motions in Microseconds via Vectorized Sampling-Based Planning

https://doi.org/10.1109/ICRA57147.2024.10611190

Thomason, Wil; Kingston, Zachary; Kavraki, Lydia E (May 2024, IEEE)

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Stochastic Implicit Neural Signed Distance Functions for Safe Motion Planning under Sensing Uncertainty

https://doi.org/10.1109/ICRA57147.2024.10610773

Quintero-Peña, Carlos; Thomason, Wil; Kingston, Zachary; Kyrillidis, Anastasios; Kavraki, Lydia E (May 2024, IEEE)

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Object Reconfiguration with Simulation-Derived Feasible Actions

https://doi.org/10.1109/ICRA48891.2023.10160377

Lee, Yiyuan; Thomason, Wil; Kingston, Zachary; Kavraki, Lydia E. (May 2023, IEEE)

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A unified sampling-based approach to integrated task and motion planning

Thomason, Wil; Knepper, Ross A. (October 2019, International Symposium on Robotics Research (ISRR))

We present a novel method for performing integrated task and motion planning (TMP) by adapting any off-the-shelf sampling-based motion planning algorithm to simultaneously solve for a symbolically and geometrically feasible plan using a single motion planner invocation. The core insight of our technique is an embedding of symbolic state into continuous space, coupled with a novel means of automatically deriving a function guiding a planner to regions of continuous space where symbolic actions can be executed. Our technique makes few assumptions and offers a great degree of flexibility and generality compared to state of the art planners. We describe our technique and offer a proof of probabilistic completeness along with empirical evaluation of our technique on manipulation benchmark problems.
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