Learning to Ground Objects for Robot Task and Motion Planning
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Task and motion planning (TAMP) algorithms have been developed to help robots plan behaviors in discrete and continuous spaces. Robots face complex real-world scenarios, where it is hardly possible to model all objects or their physical properties for robot planning (e.g., in kitchens or shopping centers). In this paper, we define a new object-centric TAMP problem, where the TAMP robot does not know object properties (e.g., size and weight of blocks). We then introduce Task-Motion Object-Centric planning (TMOC), a grounded TAMP algorithm that learns to ground objects and their physical properties with a physics engine. TMOC is particularly useful for those tasks that involve dynamic complex robot-multi-object interactions that can hardly be modeled beforehand. We have demonstrated and evaluated TMOC in simulation and using a real robot. Results show that TMOC outperforms competitive baselines from the literature in cumulative utility.
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