No abstract

For many applications, robots will need to be incrementally trained to recognize the specific objects needed for an application. This paper presents a practical system for incrementally training a robot to recognize different object categories using only a small set of visual examples provided by a human. The paper uses a recently developed state-of-theart method for few-shot incremental learning of objects. After learning the object classes incrementally, the robot performs a table cleaning task organizing objects into categories specified by the human. We also demonstrate the system's ability to learn arrangements of objects and predict missing or incorrectly placed objects. Experimental evaluations demonstrate that our approach achieves nearly the same performance as a system trained with all examples at one time (batch training), which constitutes a theoretical upper bound.

No abstract

For many real-world robotics applications, robots need to continually adapt and learn new concepts. Further, robots need to learn through limited data because of scarcity of labeled data in the realworld environments. To this end, my research focuses on developing robots that continually learn in dynamic unseen environments/scenarios, learn from limited human supervision, remember previously learned knowledge and use that knowledge to learn new concepts. I develop machine learning models that not only produce State-of-the-results on benchmark datasets but also allow robots to learn new objects and scenes in unconstrained environments which lead to a variety of novel robotics applications. INTRODUCTIONContinual adaptation and learning through limited data is the hallmark of human intelligence. Humans continue to learn new concepts over their lifetime without the need to relearn most previous concepts. With robots becoming an integral part of our society, they must also continue to learn over their lifetime to adapt to the ever-changing environments. Further, in real-world applications, robots do not have access to a large amount of labeled data since it is impractical for human users to provide hundreds of examples to the robot. Thus, robots must learn using a small amount of data through limited human supervision. The long-term goal of my research is to develop autonomous robots for everyday environments where they can learn over their lifetime and use the learned knowledge to assist humans in their daily lives.Creating robots that continually learn is a challenging problem. Deep learning is widely used to address many robot learning tasks, yet deep learning suffers from a phenomenon called catastrophic forgetting when learning continually. Catastrophic forgetting occurs when continually training a model (neural network) to recognize new classes, the model forgets the previously learned classes and the overall classification accuracy decreases. One way to address this problem is by storing the complete data of the previously learned classes. However, storing data of the previous classes requires a huge memory when learning new classes continually. Robots, on the other hand, have limited on-board memory available, hence they cannot keep storing high-dimensional images of Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

No abstract