A framework for proving the computational intractability of motion planning problems by Lynch, Jayson(Jayson R.)

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Funnel libraries for real-time robust feedback motion planning by Majumdar, Anirudha, Tedrake, Russell L

“…We consider the problem of generating motion plans for a robot that are guaranteed to succeed despite uncertainty in the environment, parametric model uncertainty, and disturbances. …”
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Asymptotically optimal kinematic design of robots using motion planning by Baykal, Cenk, Bowen, Chris, Alterovitz, Ron

“…Our method appropriately integrates sampling-based motion planning in configuration space into stochastic optimization in design space so that, over time, our evaluation of a design’s ability to reach goals increases in accuracy and our selected designs approach global optimality. …”
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FFRob: Leveraging symbolic planning for efficient task and motion planning by Garrett, Caelan Reed, Lozano-Pérez, Tomás, Kaelbling, Leslie Pack

“…Finally, we empirically demonstrate FFRob’s effectiveness on complex and diverse task and motion planning tasks including rearrangement planning and navigation among movable objects.…”
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FFRob: Leveraging symbolic planning for efficient task and motion planning by Garrett, Caelan Reed, Lozano-Pérez, Tomás, Kaelbling, Leslie P

“…Finally, we empirically demonstrate FFRob’s effectiveness on complex and diverse task and motion planning tasks including rearrangement planning and navigation among movable objects.…”
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Fast-Reactive Probabilistic Motion Planning for High-Dimensional Robots by Dai, Siyu, Hofmann, Andreas, Williams, Brian

“…Leveraging recent advances in machine learning as well as our previous work in deterministic motion planning that integrated trajectory optimization into a sparse roadmap framework, p-Chekov demonstrates its superiority in terms of collision avoidance ability and planning speed in high-dimensional robotic motion planning tasks in complex environments without the convexification of obstacles. …”
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Sampling-Based Robot Task and Motion Planning in the Real World by Garrett, Caelan Reed

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Look Before You Sweep: Visibility-Aware Motion Planning by Goretkin, Gustavo, Kaelbling, Leslie Pack, Lozano-Pérez, Tomás

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Learning compositional models of robot skills for task and motion planning by Wang, Zi, Garrett, Caelan Reed, Kaelbling, Leslie Pack, Lozano-Pérez, Tomás

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Reactive Task and Motion Planning under Temporal Logic Specifications by Li, Shen, Park, Daehyung, Sung, Yoonchang, Shah, Julie A, Roy, Nicholas

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Provably Safe and Efficient Motion Planning with Uncertain Human Dynamics by Li, Shen, Figueroa, Nadia, Shah, Ankit, Shah, Julie

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Discovering State and Action Abstractions for Generalized Task and Motion Planning by Curtis, Aidan, Silver, Tom, Tenenbaum, Joshua B, Lozano-Pérez, Tomás, Kaelbling, Leslie

“…In this paper, we propose an algorithm for learning features, abstractions, and generalized plans for continuous robotic task and motion planning (TAMP) and examine the unique difficulties that arise when forced to consider geometric and physical constraints as a part of the generalized plan. …”
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Graphs of Convex Sets with Applications to Optimal Control and Motion Planning by Marcucci, Tobia

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Real-Time Motion Planning With Applications to Autonomous Urban Driving by Kuwata, Yoshiaki, Teo, Justing, Fiore, Gaston A., Karaman, Sertac, Frazzoli, Emilio, How, Jonathan P.

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Sampling-based motion planning with deterministic u-calculus specifications by Karaman, Sertac, Frazzoli, Emilio

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Sampling-based motion planning with deterministic mu-calculus specifications by Karaman, Sertac, Frazzoli, Emilio

“…The proposed algorithms, generalizing state-of-the-art algorithms for point-to-point motion planning, incrementally build finite transition systems representing a discrete subset of dynamically feasible trajectories. …”
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A Simple Motion Planning Algorithm for General Robot Manipulators by Lozano-Perez, Tomas

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LQR-Trees: Feedback motion planning on sparse randomized trees by Tedrake, Russell Louis

“…Here we present a feedback motion planning algorithm which uses these results to efficiently combine locally valid linear quadratic regulator (LQR) controllers into a nonlinear feedback policy which probabilistically covers the reachable area of a (bounded) state space with a region of stability, certifying that all initial conditions that are capable of reaching the goal will stabilize to the goal. …”
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Real-Time Motion Planning With Applications to Autonomous Urban Driving by Kuwata, Yoshiaki, Teo, Justin, Fiore, Gaston A., Karaman, Sertac, Frazzoli, Emilio, How, Jonathan P.

“…This paper describes a real-time motion planning algorithm, based on the rapidly-exploring random tree (RRT) approach, applicable to autonomous vehicles operating in an urban environment. …”
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Robust Sampling-based Motion Planning with Asymptotic Optimality Guarantees by Karaman, Sertac, How, Jonathan P., Luders, Brandon Douglas

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