Learning grasp affordance reasoning through semantic relations
| Autor: | Èric Pairet, Ronald P. A. Petrick, Katrin Solveig Lohan, Paola Ardón, Subramanian Ramamoorthy |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Control and Optimization Grasping Computer science knowledge base systems Autonomous agent Biomedical Engineering 02 engineering and technology Computer Science - Robotics 020901 industrial engineering & automation Artificial Intelligence Human–computer interaction 0202 electrical engineering electronic engineering information engineering Affordance Mechanical Engineering GRASP humanoid robots Object (computer science) Computer Science Applications Human-Computer Interaction Control and Systems Engineering Robot 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Robotics (cs.RO) Humanoid robot |
| Zdroj: | Ramirez, P A, Pairet Artau, È, Petrick, R P A, Ramamoorthy, S & Lohan, K S 2019, ' Learning grasp affordance reasoning through semantic relations ', IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 4571-4578 . https://doi.org/10.1109/LRA.2019.2933815 |
| DOI: | 10.1109/LRA.2019.2933815 |
| Popis: | Reasoning about object affordances allows an autonomous agent to perform generalised manipulation tasks among object instances. While current approaches to grasp affordance estimation are effective, they are limited to a single hypothesis. We present an approach for detection and extraction of multiple grasp affordances on an object via visual input. We define semantics as a combination of multiple attributes, which yields benefits in terms of generalisation for grasp affordance prediction. We use Markov Logic Networks to build a knowledge base graph representation to obtain a probability distribution of grasp affordances for an object. To harvest the knowledge base, we collect and make available a novel dataset that relates different semantic attributes. We achieve reliable mappings of the predicted grasp affordances on the object by learning prototypical grasping patches from several examples. We show our method's generalisation capabilities on grasp affordance prediction for novel instances and compare with similar methods in the literature. Moreover, using a robotic platform, on simulated and real scenarios, we evaluate the success of the grasping task when conditioned on the grasp affordance prediction. Comment: Accepted in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2019 |
| Databáze: | OpenAIRE |
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