Learning Action-oriented grasping for manipulation
| Autor: | Wajahat M. Qazi, M Usman Sarwar, Jan Rosell, Muhayy Ud Din, Imran Zahoor |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. SIR - Service and Industrial Robotics |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Robòtica Process (engineering) Computer science business.industry Deep learning GRASP Stability (learning theory) 02 engineering and technology Robotics Manipulators (Mecanism) Semantics Object (computer science) Machine learning computer.software_genre 020901 industrial engineering & automation Grippers 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Artificial intelligence business Informàtica::Robòtica [Àrees temàtiques de la UPC] computer Manipuladors (Mecanismes) |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Recercat. Dipósit de la Recerca de Catalunya instname ETFA |
| Popis: | Complex manipulation tasks require grasping strategies that simultaneously satisfy the stability and the semantic constraints that have to be satisfied for an action to be feasible, referred as action-oriented semantic grasp strategies. This study develops a framework using machine learning techniques to compute action-oriented semantic grasps. It takes a 3D model of the object and the action to be performed as input and provides a vector of action-oriented semantic grasps. We evaluate the performance of machine learning (particu- larly classification techniques) to determine which approaches perform better for this problem. Using the best approaches, a multi-model classification technique is developed. The proposed approach is evaluated in simulation to grasp different kitchenobjects using a parallel gripper. The results show that multi-model classification approach enhances the prediction accuracy. The implemented system can be used as to automate the data labeling process required for deep learning approaches. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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