Depth-sensored autonomous system for table clearing tasks

Manipulation planning is a field of study with increasing interest, it combines manipulation skills and an artificial intelligence system that is able to find the optimal sequence of actions in order to solve manipulation problems. In this thesis we present a planning system for clearing a table with cluttered objects, which tackles geometrical restrictions within symbolic planning with a backtracking approach