Abstract
Current approaches combining task and motion planning require intensive geometric and symbolic reasoning to find feasible motions for task execution. The poor expressiveness of task planning domains for characterizing geometric changes with actions and the difficulties faced by current approaches to efficiently identify motion dependencies for plan execution produce expensive callings to motion planning on unfeasible actions and intensive reasoning to find realizable plans. In this work we combine two recent approaches to address these problems. Task planning is carried out using an object-centered description of geometric relations that consistently characterizes changes in the object configuration space. Plan execution is implemented using a symbol to motion hierarchical decomposition that depends on consecutive actions in the plan, rather than on single actions, which permits considering motion dependencies across plan actions for a successful execution.
| Original language | English |
|---|---|
| Article number | 9140305 |
| Pages (from-to) | 5629-5636 |
| Number of pages | 8 |
| Journal | IEEE Robotics and Automation Letters |
| Volume | 5 |
| Issue number | 4 |
| DOIs | |
| State | Published - Oct 2020 |
Keywords
- AI-Based methods
- cognitive control architectures
- manipulation planning
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