2026-04-13T13:14:12Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/187672026-01-23T03:58:08Zcom_2072_1033col_2072_452950

POMDP approach to robotized clothes separation Monsó Purtí, Pol Alenyà Ribas, Guillem Torras, Carme Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial artificial Artificial intelligence Planning Uncertainty (Information theory) planning (artificial intelligence) uncertainty handling Intel·ligència artificial Planificació Incertitud (Teoria de la informació) Classificació INSPEC::Cybernetics::Artificial intelligence::Planning (artificial intelligence) Classificació INSPEC::Cybernetics::Artificial intelligence::Uncertainty handling 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. Peer Reviewed Postprint (author’s final draft) 2012 Conference report http://dx.doi.org/10.1109/IROS.2012.6386011 info:eu-repo/grantAgreement/EC/FP7/269959/EU/Intelligent observation and execution of Actions and manipulations/INTELLACT http://creativecommons.org/licenses/by-nc-nd/3.0/es/ Open Access Attribution-NonCommercial-NoDerivs 3.0 Spain