Planning efficiently at task and motion levels allows the setting of new challenges for robotic manipulation problems, like for instance constrained table-top problems for bi-manual robots. In this scope, the appropriate combination of task and motion planning levels plays an important role. Accordingly, a heuristic-based task and motion planning approach is proposed, in which the computation of the heuristic addresses a geometrically relaxed problem, i.e., it only reasons upon objects placements, grasp poses, and inverse kinematics solutions. Motion paths are evaluated lazily, i.e., only after an action has been selected by the heuristic. This reduces the number of calls to the motion planner, while backtracking is reduced because the heuristic captures most of the geometric constraints. The approach has been validated in simulation and on a real robot, with different classes of table-top manipulation problems. Empirical comparison with recent approaches solving similar problems is also reported, showing that the proposed approach results in significant improvement both in terms of planing time and success rate.
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This work is partially supported by the Spanish Government through the Project DPI2016-80077-R. It is also supported by Swedish Knowledge Foundation (KKS) Project “Semantic Robots”. Aliakbar Akbari is supported by the Spanish Government through the Grant FPI 2015.
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Akbari, A., Lagriffoul, F. & Rosell, J. Combined heuristic task and motion planning for bi-manual robots. Auton Robot 43, 1575–1590 (2019). https://doi.org/10.1007/s10514-018-9817-3
- Combined task and motion planning
- Robot manipulation
- Geometric reasoning
- Path planning