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Learning object affordances by leveraging the combination of human-guidance and self-exploration | IEEE Conference Publication | IEEE Xplore

Learning object affordances by leveraging the combination of human-guidance and self-exploration


Abstract:

Our work focuses on robots to be deployed in human environments. These robots, which will need specialized object manipulation skills, should leverage end-users to effici...Show More

Abstract:

Our work focuses on robots to be deployed in human environments. These robots, which will need specialized object manipulation skills, should leverage end-users to efficiently learn the affordances of objects in their environment. This approach is promising because people naturally focus on showing salient aspects of the objects [1]. We replicate prior results and build on them to create a combination of self and supervised learning. We present experimental results with a robot learning 5 affordances on 4 objects using 1219 interactions. We compare three conditions: (1) learning through self-exploration, (2) learning from supervised examples provided by 10 naïve users, and (3) self-exploration biased by the user input. Our results characterize the benefits of self and supervised affordance learning and show that a combined approach is the most efficient and successful.
Date of Conference: 07-10 March 2016
Date Added to IEEE Xplore: 14 April 2016
ISBN Information:
Electronic ISSN: 2167-2148
Conference Location: Christchurch, New Zealand

References

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