Online Object and Task Learning via Human Robot Interaction
By Masood Dehghan, Mennatullah Siam, Jun Jin, Laura C. Petrich, Martin Jägersand. 2018
33 citations recorded by the source corpus.
This work describes the development of a robotic system that acquires knowledge incrementally through human interaction where new objects and motions are taught on the fly. The robotic system developed was one of the five finalists in the KUKA Innovation Award competition and demonstrated during the Hanover Messe 2018 in Germany. The main contributions of the system are i) a novel incremental object learning module - a deep learning based localization and recognition system - that allows a human to teach new objects to the robot, ii) an intuitive user interface for specifying 3D motion task associated with the new object, and iii) a hybrid force-vision control module for performing compliant motion on an unstructured surface. This paper describes the implementation and integration of the main modules of the system and summarizes the lessons learned from the competition.
Subfields: Aerospace Engineering, Control and Systems Engineering
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Citations in the corpus
- Few-Shot Continual Active Learning by a Robot (2022, methodology): “Incremental classifier and representation learning (iCaRL) [3] combines knowledge distillation [44] and NCM for class-incremental learning.”
- Continual Learning for Real-World Autonomous Systems: Algorithms, Challenges and Frameworks (2021, background): “6 Online Object and Task Learning via Human Robot Interaction [54]”
- Tell me what this is: Few-Shot Incremental Object Learning by a Robot (2020, methodology): “An extension of [15] is presented in [18] for incremental object learning for a robot using a combination of a pre-trained feature extractor and NCM classifier which has been shown to be significantly inferior to other incremental learning approaches [3], [5].”