State lattice with controllers: Augmenting lattice-based path planning with controller-based motion primitives

Butzke, Jonathan; Sapkota, Krishna; Prasad, K.T.; MacAllister, Brian; Likhachev, Maxim

doi:10.1109/iros.2014.6942570

Cited by 17 publications

(6 citation statements)

References 27 publications

(30 reference statements)

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“…Parallelized versions of RRT have also been developed in which multiple cores expand the search tree by sampling and adding multiple new states in parallel (Devaurs, Siméon, and Cortés 2011;Ichnowski and Alterovitz 2012;Jacobs et al 2013;Park, Pan, and Manocha 2016). However, in a lot of planning domains involving planning with controllers (Butzke et al 2014), sampling of states is typically not possible. One such class of planning domains where state sampling is not possible is simulator-in-the-loop planning, which uses an expensive physics simulator to generate successors (Liang et al 2021).…”

Section: Parallel Sampling-based Algorithmsmentioning

confidence: 99%

ePASE: Edge-based Parallel A for Slow Evaluations

Mukherjee¹,

Aine²,

Likhachev³

2022

Preprint

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Parallel search algorithms harness the multithreading capability of modern processors to achieve faster planning. One such algorithm is PA*SE (Parallel A* for Slow Expansions), which parallelizes state expansions to achieve faster planning in domains where state expansions are slow. In this work, we propose ePA*SE (Edge-based Parallel A* for Slow Evaluations) that improves on PA*SE by parallelizing edge evaluations instead of state expansions. This makes ePA*SE more efficient in domains where edge evaluations are expensive and need varying amounts of computational effort, which is often the case in robotics. On the theoretical front, we show that ePA*SE provides rigorous optimality guarantees. In addition, ePA*SE can be trivially extended to handle an inflation weight on the heuristic resulting in a bounded suboptimal algorithm w-ePA*SE (Weighted ePA*SE) that trades off optimality for faster planning. On the experimental front, we validate the proposed algorithm in two different planning domains: 1) motion planning for 3D humanoid navigation and 2) task and motion planning for a dual-arm robotic assembly task. We show that ePA*SE can be significantly more efficient than PA*SE and other alternatives.

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Section: Parallel Sampling-based Algorithmsmentioning

confidence: 99%

ePASE: Edge-based Parallel A for Slow Evaluations

Mukherjee¹,

Aine²,

Likhachev³

2022

Preprint

Self Cite

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“…This can be prohibitively expensive to perform online, depending on the complexity of simulation and the duration of each skill. To avoid simulation rollouts, works have used hardcoded analytical [22], [23] or symbolic [24], [25], [26] skill effect models. Manually engineering such models may not always be feasible, and they do not easily scale to changes in skills, dynamics, and tasks.…”

Section: Related Workmentioning

confidence: 99%

Search-Based Task Planning with Learned Skill Effect Models for Lifelong Robotic Manipulation

Liang¹,

Sharma²,

Alex³

et al. 2021

Preprint

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Lifelong-learning robots need to be able to acquire new skills and plan for new tasks over time. Prior works on planning with skills often make assumptions on the structure of skills and tasks, like subgoal skills, shared skill implementations, or learning task-specific plan skeletons, that limit their application to new and different skills and tasks. By contrast, we propose doing task planning by jointly searching in the space of skills and their parameters with skill effect models learned in simulation. Our approach is flexible about skill parameterizations and task specifications, and we use an iterative training procedure to efficiently generate relevant data to train such models. Experiments demonstrate the ability of our planner to integrate new skills in a lifelong manner, finding new task strategies with lower costs in both train and test tasks. We additionally show that our method can transfer to the real world without further fine-tuning.

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“…Further, its asymptotic optimality, meaning almost-sure convergence to the optimal solution, ensures higher-quality motion plans, at least when using high sampling density. In cases where the non-holonomic robot dynamics have non-negligible effects on estimated travel costs, the use of motion primitives (Butzke et al, 2014), or tree-based approaches which forward simulate robot motion are required. It should be noted that PETLON is not restricted to specific task or motion planning algorithms.…”

Section: Algorithm Instantiationmentioning

confidence: 99%

The PETLON Algorithm to Plan Efficiently for Task-Level-Optimal Navigation

Zhang²,

Stone³

2020

jair

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Intelligent mobile robots have recently become able to operate autonomously in large-scale indoor environments for extended periods of time. In this process, mobile robots need the capabilities of both task and motion planning. Task planning in such environments involves sequencing the robot’s high-level goals and subgoals, and typically requires reasoning about the locations of people, rooms, and objects in the environment, and their interactions to achieve a goal. One of the prerequisites for optimal task planning that is often overlooked is having an accurate estimate of the actual distance (or time) a robot needs to navigate from one location to another. State-of-the-art motion planning algorithms, though often computationally complex, are designed exactly for this purpose of finding routes through constrained spaces. In this article, we focus on integrating task and motion planning (TMP) to achieve task-level-optimal planning for robot navigation while maintaining manageable computational efficiency. To this end, we introduce TMP algorithm PETLON (Planning Efficiently for Task-Level-Optimal Navigation), including two configurations with different trade-offs over computational expenses between task and motion planning, for everyday service tasks using a mobile robot. Experiments have been conducted both in simulation and on a mobile robot using object delivery tasks in an indoor office environment. The key observation from the results is that PETLON is more efficient than a baseline approach that pre-computes motion costs of all possible navigation actions, while still producing plans that are optimal at the task level. We provide results with two different task planning paradigms in the implementation of PETLON, and offer TMP practitioners guidelines for the selection of task planners from an engineering perspective.

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State lattice with controllers: Augmenting lattice-based path planning with controller-based motion primitives

Cited by 17 publications

References 27 publications

ePASE: Edge-based Parallel A for Slow Evaluations

ePASE: Edge-based Parallel A for Slow Evaluations

Search-Based Task Planning with Learned Skill Effect Models for Lifelong Robotic Manipulation

The PETLON Algorithm to Plan Efficiently for Task-Level-Optimal Navigation

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State lattice with controllers: Augmenting lattice-based path planning with controller-based motion primitives

Cited by 17 publications

References 27 publications

ePA*SE: Edge-based Parallel A* for Slow Evaluations

ePA*SE: Edge-based Parallel A* for Slow Evaluations

Search-Based Task Planning with Learned Skill Effect Models for Lifelong Robotic Manipulation

The PETLON Algorithm to Plan Efficiently for Task-Level-Optimal Navigation

Contact Info

Product

Resources

About

ePASE: Edge-based Parallel A for Slow Evaluations

ePASE: Edge-based Parallel A for Slow Evaluations