Richard Kelley

Understanding human intentions via hidden markov models in autonomous mobile robots

Understanding intent is an important aspect of communication among people and is an essential component of the human cognitive system. This capability is particularly relevant for situations that involve collaboration among agents or detection of situations that can pose a threat. In this paper, we propose an approach that allows a robot to detect intentions of others based on experience acquired through its own sensory-motor capabilities, then using this experience while taking the perspective of the agent whose intent should be recognized. Our method uses a novel formulation of Hidden Markov Models designed to model a robots experience and interaction with the world. The robots capability to observe and analyze the current scene employs a novel vision-based technique for target detection and tracking, using a non-parametric recursive modeling approach. We validate this architecture with a physically embedded robot, detecting the intent of several people performing various activities.

AN ARCHITECTURE FOR UNDERSTANDING INTENT USING A NOVEL HIDDEN MARKOV FORMULATION

Understanding intent is an important aspect of communication among people and is an essential component of the human cognitive system. This capability is particularly relevant to situations that involve collaboration among multiple agents or detection of situations that can pose a particular threat. In this paper, we propose an approach that allows a physical robot to detect the intent of others based on experience acquired through its own sensory–motor capabilities, then use this experience while taking the perspective of the agent whose intent should be recognized. Our method uses a novel formulation of hidden Markov models (HMMs) designed to model a robot’s experience and interaction with the world when performing various actions. The robot’s capability to observe and analyze the current scene employs a novel vision-based technique for target detection and tracking, using a nonparametric recursive modeling approach. We validate this architecture with a physically embedded robot, detecting the intent of several people performing various activities.

Microservice-based architecture for the NRDC

The NSF EPSCOR funded Solar Nexus Project is a collaborative effort between scientists, engineers, educators, and technicians to increase the amount of renewable solar energy in Nevada while eliminating its adverse effects on the surrounding environment and wildlife, and minimizing water consumption. The project seeks to research multiple areas, including water usage at power plants, the effect of power plant construction on the surrounding ecology, alternative wastewater methods to maintain solar panels, and interdisciplinary solutions to improve solar energy in Nevada. In order to organize and analyze this data to produce effective change, Nexus needs a centralized database to store collected data. To this end the Nevada Research Data Center is designed to collect, format, and store data for scientists to view and consider. This paper presents a new architecture solution for the NRDC. Based in microservices, the solution aims to ensure scalability, reliability, and maintainability of this data center. Background on NRDC is provided in the paper, together with details on the proposed solutions software specification, design, and prototype implementation. A discussion of the microservice-based architectures benefits and an outline of planned directions of future work are also included.

Context-Based Bayesian Intent Recognition

One of the foundations of social interaction among humans is the ability to correctly identify interactions and infer the intentions of others. To build robots that reliably function in the human social world, we must develop models that robots can use to mimic the intent recognition skills found in humans. We propose a framework that uses contextual information in the form of object affordances and object state to improve the performance of an underlying intent recognition system. This system represents objects and their affordances using a directed graph that is automatically extracted from a large corpus of natural language text. We validate our approach on a physical robot that classifies intentions in a number of scenarios.

Deep networks for predicting human intent with respect to objects

Effective human-robot interaction requires systems that can accurately infer and predict human intentions. In this paper, we introduce a system that uses stacked denoising autoencoders to perform intent recognition. We introduce the intent recognition problem, provide an overview of deep architectures in machine learning, and outline the components of our system. We also provide preliminary results for our systems performance.

A vision-based architecture for intent recognition

Understanding intent is an important aspect of communication among people and is an essential component of the human cognitive system. This capability is particularly relevant for situations that involve collaboration among multiple agents or detection of situations that can pose a particular threat. We propose an approach that allows a physical robot to detect the intentions of others based on experience acquired through its own sensory-motor abilities. It uses this experience while taking the perspective of the agent whose intent should be recognized. The robots capability to observe and analyze the current scene employs a novel vision-based technique for target detection and tracking, using a non-parametric recursive modeling approach. Our intent recognition method uses a novel formulation of Hidden Markov Models (HMMs) designed to model a robots experience and its interaction with the world while performing various actions.

An Unsupervised Approach to Learning and Early Detection of Spatio-Temporal Patterns Using Spiking Neural Networks

This paper addresses the problem of learning and recognizing spatio-temporal patterns, which are typically encountered when representing gestures or other human actions. Existing approaches to learning such patterns are typically supervised, rely on extensive amounts of training data and require the observation of the entire pattern for recognition. We propose an approach that brings the following main contributions: i) it learns the patterns in an unsupervised manner, ii) it uses a very small number of training samples, and iii) it enables early classification of the pattern from observing only a small fraction of the pattern. The proposed method relies on spiking networks with axonal conductance delays, which learn encoding of individual patterns as sets of polychronous neural groups. Classification is performed using a similarity metric between sets, based on a modified version of the Jaccard index. The approach is evaluated on a data set of hand-drawn digits that encode the temporal information on how the digit has been drawn. In addition, the method is compared with three other standard pattern classification methods: support vector machines, logistic regression with regularization and ensemble neural networks, all trained with the same data set. The results show that the proposed approach can successfully learn these patterns from a significantly small number of training samples, can identify patterns before their completion, and it performs better than or comparable with the three other supervised methods.

A Scale and Translation Invariant Approach for Early Classification of Spatio-Temporal Patterns Using Spiking Neural Networks

This paper addresses the problem of encoding and classifying spatio-temporal patterns, which are typical for human actions or gestures. The proposed method has the following main contributions: (i) it requires a very small number of training examples, (ii) it accepts variable sized input patterns, (iii) it is invariant to scale and translation, and (iv) it enables early recognition, from only partial information of the pattern. The underlying representation employed is a spiking neural network with axonal conductance delay. We designed a novel approach for mapping spatio-temporal patterns to spike trains, which are used to stimulate the network. The pattern features emerge in the network as a result of this stimulation in the form of polychronous neuronal groups, which are used for classification. The proposed method is validated on a set of gestures representing the digits from

Robots as animals: A framework for liability and responsibility in human-robot interactions

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