Representation, learning, and planning algorithms for geometric task and motion planning
Autor: | Beomjoon Kim, Leslie Pack Kaelbling, Luke Shimanuki, Tomás Lozano-Pérez |
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Rok vydání: | 2021 |
Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning 0209 industrial biotechnology Computer Science - Artificial Intelligence Graph neural networks Computer science 02 engineering and technology Machine Learning (cs.LG) Task (project management) Computer Science - Robotics 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Motion planning Electrical and Electronic Engineering Planning algorithms business.industry Applied Mathematics Mechanical Engineering Artificial Intelligence (cs.AI) Modeling and Simulation 020201 artificial intelligence & image processing Artificial intelligence business Robotics (cs.RO) Feature learning Software |
Zdroj: | The International Journal of Robotics Research. 41:210-231 |
ISSN: | 1741-3176 0278-3649 |
Popis: | We present a framework for learning to guide geometric task-and-motion planning (G-TAMP). G-TAMP is a subclass of task-and-motion planning in which the goal is to move multiple objects to target regions among movable obstacles. A standard graph search algorithm is not directly applicable, because G-TAMP problems involve hybrid search spaces and expensive action feasibility checks. To handle this, we introduce a novel planner that extends basic heuristic search with random sampling and a heuristic function that prioritizes feasibility checking on promising state–action pairs. The main drawback of such pure planners is that they lack the ability to learn from planning experience to improve their efficiency. We propose two learning algorithms to address this. The first is an algorithm for learning a rank function that guides the discrete task-level search, and the second is an algorithm for learning a sampler that guides the continuous motion-level search. We propose design principles for designing data-efficient algorithms for learning from planning experience and representations for effective generalization. We evaluate our framework in challenging G-TAMP problems, and show that we can improve both planning and data efficiency. |
Databáze: | OpenAIRE |
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