A Long Horizon Planning Framework for Manipulating Rigid Pointcloud Objects

• URL: http://arxiv.org/abs/2011.08177v1
• Date: Mon, 16 Nov 2020 18:59:33 GMT
• Title: A Long Horizon Planning Framework for Manipulating Rigid Pointcloud Objects
• Authors: Anthony Simeonov, Yilun Du, Beomjoon Kim, Francois R. Hogan, Joshua Tenenbaum, Pulkit Agrawal, Alberto Rodriguez
• Abstract summary: We present a framework for solving long-horizon planning problems involving manipulation of rigid objects. Our method plans in the space of object subgoals and frees the planner from reasoning about robot-object interaction dynamics.
• Score: 25.428781562909606
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a framework for solving long-horizon planning problems involving manipulation of rigid objects that operates directly from a point-cloud observation, i.e. without prior object models. Our method plans in the space of object subgoals and frees the planner from reasoning about robot-object interaction dynamics by relying on a set of generalizable manipulation primitives. We show that for rigid bodies, this abstraction can be realized using low-level manipulation skills that maintain sticking contact with the object and represent subgoals as 3D transformations. To enable generalization to unseen objects and improve planning performance, we propose a novel way of representing subgoals for rigid-body manipulation and a graph-attention based neural network architecture for processing point-cloud inputs. We experimentally validate these choices using simulated and real-world experiments on the YuMi robot. Results demonstrate that our method can successfully manipulate new objects into target configurations requiring long-term planning. Overall, our framework realizes the best of the worlds of task-and-motion planning (TAMP) and learning-based approaches. Project website: https://anthonysimeonov.github.io/rpo-planning-framework/.

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