The State of Lifelong Learning in Service Robots: Current Bottlenecks in Object Perception and Manipulation

• URL: http://arxiv.org/abs/2003.08151v3
• Date: Thu, 6 May 2021 18:58:38 GMT
• Title: The State of Lifelong Learning in Service Robots: Current Bottlenecks in Object Perception and Manipulation
• Authors: S. Hamidreza Kasaei, Jorik Melsen, Floris van Beers, Christiaan Steenkist, and Klemen Voncina
• Abstract summary: State-of-the-art continues to improve to make a proper coupling between object perception and manipulation. In most of the cases, robots are able to recognize various objects, and quickly plan a collision-free trajectory to grasp a target object. In such environments, no matter how extensive the training data used for batch learning, a robot will always face new objects. apart from robot self-learning, non-expert users could interactively guide the process of experience acquisition.
• Score: 3.7858180627124463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Service robots are appearing more and more in our daily life. The development of service robots combines multiple fields of research, from object perception to object manipulation. The state-of-the-art continues to improve to make a proper coupling between object perception and manipulation. This coupling is necessary for service robots not only to perform various tasks in a reasonable amount of time but also to continually adapt to new environments and safely interact with non-expert human users. Nowadays, robots are able to recognize various objects, and quickly plan a collision-free trajectory to grasp a target object in predefined settings. Besides, in most of the cases, there is a reliance on large amounts of training data. Therefore, the knowledge of such robots is fixed after the training phase, and any changes in the environment require complicated, time-consuming, and expensive robot re-programming by human experts. Therefore, these approaches are still too rigid for real-life applications in unstructured environments, where a significant portion of the environment is unknown and cannot be directly sensed or controlled. In such environments, no matter how extensive the training data used for batch learning, a robot will always face new objects. Therefore, apart from batch learning, the robot should be able to continually learn about new object categories and grasp affordances from very few training examples on-site. Moreover, apart from robot self-learning, non-expert users could interactively guide the process of experience acquisition by teaching new concepts, or by correcting insufficient or erroneous concepts. In this way, the robot will constantly learn how to help humans in everyday tasks by gaining more and more experiences without the need for re-programming.

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