Richard G. Freedman

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Interaction between multiple agents requires some form of coordination and a level of mutual awareness. When computers and robots interact with people, they need to recognize human plans and react appropriately. Plan and goal recognition techniques have focused on identifying an agent’s task given a sufficiently long action sequence. However, by the time(More)
We examine new ways to perform plan recognition (PR) using natural language processing (NLP) techniques. PR often focuses on the structural relationships between consecutive observations and ordered activities that comprise plans. However, NLP commonly treats text as a bag-of-words, omitting such structural relationships and using topic models to break down(More)
The use of robots in stroke rehabilitation has become a popular trend in rehabilitation robotics. However, despite the acknowledged value of customized service for individual patients, research on programming adaptive therapy for individual patients has received little attention. The goal of the current study is to model teletherapy sessions in the form of(More)
I. INTRODUCTION For robots to properly interact with humans, it is important that they are able to recognize users' plans and activities so that they may respond accordingly. Many activity recognition (AR) algorithms involve signal processing [1] or supervised learning [2], [3] to label raw sensor data with a human-defined action, but these methods restrict(More)
We consider ways to improve the performance of unsupervised plan and activity recognition techniques by considering temporal and object relations in addition to postural data. Temporal relationships can help recognize activities with cyclic structure and are often implicit because plans have degrees of ordering actions. Relations with objects can help(More)
Plan recognition (PR) and activity recognition (AR) systems are essential for effective human-robot interaction (HRI) since the robot needs to predict what other agents in the environment are doing (Lösch et al. 2007). Even when robots are designed to perform simple tasks such as lending an object to a person (Levine and Williams 2014), they cannot follow(More)
The ability to identify what humans are doing in the environment is a crucial element of responsive behavior in humanrobot interaction. We examine new ways to perform plan recognition (PR) using natural language processing (NLP) techniques. PR often focuses on the structural relationships between consecutive observations and ordered activities that comprise(More)
Unsupervised machine learning methods are useful for identifying clusters of similar inputs with respect to some criteria and giving the inputs within each cluster the same label. However, the results of many such methods rely on parameter choices that can alter the derived classification labels for each input. Verification methods for determining the(More)
Contemporary research in human-robot interaction (HRI) predominantly focuses on the user’s experience while controlling a robot. However, with the increased deployment of artificial intelligence (AI) techniques, robots are quickly becoming more autonomous in both academic and industrial experimental settings. In addition to improving the user’s interactive(More)