Thursday, 12th of November 2020, 12:00 – 1:00

Task planning for robotics using object-centered predicates and action contexts


Alejandro Agostini
Researcher at IIS group (University of Innsbruck)


Symbolic planning is a useful problem-solving paradigm that finds sequences of operators (or plans) that progressively transform the world state until a goal is achieved. It uses well-known artificial intelligence (AI) searching techniques that encode task structures into states and actions using a human-readable notation. This makes it particularly appealing for robotic executions of human-like tasks, allowing a lay person to naturally specify the task to a robot (e.g. set a table) while letting the robot automatically generate the sequence of instructions to complete it. However, symbolic planning and robotic techniques use different representations and search strategies, which poses serious difficulties when these paradigms should be combined into a single framework. Current approaches tackle this problem by defining ad-hoc solutions for particular laboratory conditions that lead to the generation of physically impractical plans or that require intensive computation to transform a plan into feasible robot motions. To address this problem we propose a symbolic domain representation that consistently encodes relevant geometric constraints to favour the generation of physically feasible plans. These constraints are described using an object-centered perspective that can be directly linked to robot sensing parameters (e.g. object poses) without handcrafting symbol-signal relations. For plan execution, we evaluate the context of a symbolic action in the plan to infer its actual intention, e.g. pick a bottle with the intention of pouring afterwards, which permits selecting suitable acting parameters for the generation of robot motions.


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