Acquisition of Viewpoint Transformation and Action Mappings via Sequence to Sequence Imitative Learning by Deep Neural Networks

We propose an imitative learning model that allows a robot to acquire positional relations between the demonstrator and the robot, and to transform observed actions into robotic actions. Providing robots with imitative capabilities allows us to teach novel actions to them without resorting to trial-and-error approaches. Existing methods for imitative robotic learning require mathematical formulations or conversion modules to translate positional relations between demonstrators and robots. The proposed model uses two neural networks, a convolutional autoencoder (CAE) and a multiple timescale recurrent neural network (MTRNN). The CAE is trained to extract visual features from raw images captured by a camera. The MTRNN is trained to integrate sensory-motor information and to predict next states. We implement this model on a robot and conducted sequence to sequence learning that allows the robot to transform demonstrator actions into robot actions. Through training of the proposed model, representations of actions, manipulated objects, and positional relations are formed in the hierarchical structure of the MTRNN. After training, we confirm capability for generating unlearned imitative patterns

Mirroring to Build Trust in Digital Assistants

We describe experiments towards building a conversational digital assistant that considers the preferred conversational style of the user. In particular, these experiments are designed to measure whether users prefer and trust an assistant whose conversational style matches their own. To this end we conducted a user study where subjects interacted with a digital assistant that responded in a way that either matched their conversational style, or did not. Using self-reported personality attributes and subjects' feedback on the interactions, we built models that can reliably predict a user's preferred conversational style.Comment: Preprin

The development of bottom-up and top-down interaction in the processing of goal-directed action

The study of action-cognition is driven by the assumption that what one can do motorically depends on what one can conceive of mentally, given a set of external opportunities (Rosenbaum, Cohen, & Jax, 2007). Therefore, a comprehensive theory of action development ought to integrate perceptual aspects of action processing with conceptual changes that give rise to increasingly abstract behaviours. How and why children progress to higher levels of organization in the processing and coordination of purposeful behaviour is a question that has been at the core of developmental research for decades. Various competences underlying early action processing and decision-making have been identified and linked to sophisticated mental operations later in life. However, considerably less is known about the relationships between perceptual and conceptual abilities and how they interact to shape action development. Goal-pursuit is achieved with increasing efficiency during the preschool period. In fact, by the age of first grade children show substantial abilities to regulate actions into hierarchically structured sequences of events that can be transferred across contexts (e.g., Levy, 1980; Bell & Livesey, 1985; Livesey & Morgan, 1991). The aim of this project was to investigate the perceptual and conceptual processes that drive these remarkable advances as they emerge during the preschool years. The studies in this thesis investigate top-down and bottom-up interactions in the processing of actions at various levels of abstraction. Employing a range of novel paradigms, the results of four studies highlight considerable advances in preschoolers’ abilities to organise actions in terms of goal hierarchies. Findings further highlight that the ability to extract structure at a basic level is readily achieved early in life, while higher-level action comprehension and planning abilities continue to develop throughout the childhood years

A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator

Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 2

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Texas, Houston. Topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making

A MULTIPLE REPRESENTATIONS MODEL OF THE HUMAN MIRROR NEURON SYSTEM FOR LEARNED ACTION IMITATION

The human mirror neuron system (MNS) is a fundamental sensorimotor system that plays a critical role in action observation and imitation. Despite a large body of experimental and theoretical MNS studies, the visuospatial transformation between the observed and the imitated actions has received very limited attention. Therefore, this work proposes a neurobiologically plausible MNS model, which examines the dynamics between the fronto-parietal mirror system and the parietal visuospatial transformation system during action observation and imitation. The fronto-parietal network is composed of the inferior frontal gyrus (IFG) and the inferior parietal lobule (IPL), which are postulated to generate the neural commands and the predictions for its sensorimotor consequences, respectively. The parietal regions identified as the superior parietal lobule (SPL) and the intraparietal sulcus (IPS) are postulated to encode the visuospatial transformation for enabling view-independent representations of the observed action. The middle temporal region is postulated to provide the view-dependent representations such as direction and velocity of the observed action. In this study, the SPL/IPS, IFG, and IPL are modeled with artificial neural networks to simulate the neural mechanisms underlying action imitation. The results reveal that this neural model can replicate relevant behavioral and neurophysiological findings obtained from previous action imitation studies. Specifically, the imitator can replicate the observed actions independently of the spatial relationships with the demonstrator while generating similar synthetic functional magnetic resonance imaging blood oxygenation level-dependent responses in the IFG for both action observation and execution. Moreover, the SPL/IPS can provide view-independent visual representations through mental transformation for which the response time monotonically increases as the rotation angle augments. Furthermore, the simulated neural activities reveal the emergence of both view-independent and view-dependent neural populations in the IFG. As a whole, this work suggests computational mechanisms by which visuospatial transformation processes would subserve the MNS for action observation and imitation independently of the differences in anthropometry, distance, and viewpoint between the demonstrator and the imitator

Generative Models for Learning Robot Manipulation Skills from Humans

A long standing goal in artificial intelligence is to make robots seamlessly interact with humans in performing everyday manipulation skills. Learning from demonstrations or imitation learning provides a promising route to bridge this gap. In contrast to direct trajectory learning from demonstrations, many problems arise in interactive robotic applications that require higher contextual level understanding of the environment. This requires learning invariant mappings in the demonstrations that can generalize across different environmental situations such as size, position, orientation of objects, viewpoint of the observer, etc. In this thesis, we address this challenge by encapsulating invariant patterns in the demonstrations using probabilistic learning models for acquiring dexterous manipulation skills. We learn the joint probability density function of the demonstrations with a hidden semi-Markov model, and smoothly follow the generated sequence of states with a linear quadratic tracking controller. The model exploits the invariant segments (also termed as sub-goals, options or actions) in the demonstrations and adapts the movement in accordance with the external environmental situations such as size, position and orientation of the objects in the environment using a task-parameterized formulation. We incorporate high-dimensional sensory data for skill acquisition by parsimoniously representing the demonstrations using statistical subspace clustering methods and exploit the coordination patterns in latent space. To adapt the models on the fly and/or teach new manipulation skills online with the streaming data, we formulate a non-parametric scalable online sequence clustering algorithm with Bayesian non-parametric mixture models to avoid the model selection problem while ensuring tractability under small variance asymptotics. We exploit the developed generative models to perform manipulation skills with remotely operated vehicles over satellite communication in the presence of communication delays and limited bandwidth. A set of task-parameterized generative models are learned from the demonstrations of different manipulation skills provided by the teleoperator. The model captures the intention of teleoperator on one hand and provides assistance in performing remote manipulation tasks on the other hand under varying environmental situations. The assistance is formulated under time-independent shared control, where the model continuously corrects the remote arm movement based on the current state of the teleoperator; and/or time-dependent autonomous control, where the model synthesizes the movement of the remote arm for autonomous skill execution. Using the proposed methodology with the two-armed Baxter robot as a mock-up for semi-autonomous teleoperation, we are able to learn manipulation skills such as opening a valve, pick-and-place an object by obstacle avoidance, hot-stabbing (a specialized underwater task akin to peg-in-a-hole task), screw-driver target snapping, and tracking a carabiner in as few as 4 - 8 demonstrations. Our study shows that the proposed manipulation assistance formulations improve the performance of the teleoperator by reducing the task errors and the execution time, while catering for the environmental differences in performing remote manipulation tasks with limited bandwidth and communication delays

The development of bottom-up and top-down interaction in the processing of goal-directed action

The study of action-cognition is driven by the assumption that what one can do motorically depends on what one can conceive of mentally, given a set of external opportunities (Rosenbaum, Cohen, & Jax, 2007). Therefore, a comprehensive theory of action development ought to integrate perceptual aspects of action processing with conceptual changes that give rise to increasingly abstract behaviours. How and why children progress to higher levels of organization in the processing and coordination of purposeful behaviour is a question that has been at the core of developmental research for decades. Various competences underlying early action processing and decision-making have been identified and linked to sophisticated mental operations later in life. However, considerably less is known about the relationships between perceptual and conceptual abilities and how they interact to shape action development. Goal-pursuit is achieved with increasing efficiency during the preschool period. In fact, by the age of first grade children show substantial abilities to regulate actions into hierarchically structured sequences of events that can be transferred across contexts (e.g., Levy, 1980; Bell & Livesey, 1985; Livesey & Morgan, 1991). The aim of this project was to investigate the perceptual and conceptual processes that drive these remarkable advances as they emerge during the preschool years. The studies in this thesis investigate top-down and bottom-up interactions in the processing of actions at various levels of abstraction. Employing a range of novel paradigms, the results of four studies highlight considerable advances in preschoolers’ abilities to organise actions in terms of goal hierarchies. Findings further highlight that the ability to extract structure at a basic level is readily achieved early in life, while higher-level action comprehension and planning abilities continue to develop throughout the childhood years