Rigid object manipulation with robots has mainly relied on precise, expensive models and deterministic sequences. Given the great complexity of accurately modeling deformable objects, their manipulation seems to call for a rather different approach. This paper proposes a probabilistic planner, based on a Partially Observable Markov Decision Process (POMDP), targeted at reducing the inherent uncertainty of deformable object sorting. It is shown that a small set of unreliable actions and inaccurate perceptions suffices to accomplish the task, provided faithful statistics on both of them are collected beforehand. The planner has been applied to a clothes sorting task in a real case context with a depth and color sensor and a robotic arm. Experimental results show the promise of the approach since more than 95% certainty of having isolated a piece of clothing is reached in an average of four steps for quite entangled initial clothing configurations.


planning (artificial intelligence), uncertainty handling.

Scientific reference

P. Monsó, G. Alenyà and C. Torras. POMDP approach to robotized clothes separation, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012, Vilamoura, Portugal, pp. 1324-1329, IEEE.