Dynamic Touch as Common Ground for Enactivism and Ecological Psychology

The main purpose of this article is to show that enactivism and ecological psychology share more aspects than is often recognized. Rather than debating about differences, commonalities between the approaches are illustrated with the example of dynamic touch. Dynamic touch is a form of touch that implies muscles and tendons and that allows the perception of hand-held objects that are wielded but not seen. Given that perceivers perform the wielding movements with effort, dynamic touch necessarily implies active exploration. The strength of dynamic touch as an example lies in the fact that it has been formalized and analyzed in detail at the level of the laws that govern the organism-environment system. The example provides empirically supported instantiations of sensorimotor contingencies, in enactivist terms, and of intentional exploration and information detection, in ecological terms. Moreover, dynamic touch is a practical example of the enactivist concepts of bringing-forth the world and sense-making. As a second purpose, we use the example of dynamic touch to clarify key concepts of the ecological approach. Specifically, we analyze the concepts of invariance and affordance, indicating the crucial difference between perceiving and actualizing affordances, and highlighting the importance of these concepts for the dialogue between enactivism and ecological psychology.2019-2

Continually improving grounded natural language understanding through human-robot dialog

As robots become ubiquitous in homes and workplaces such as hospitals and factories, they must be able to communicate with humans. Several kinds of knowledge are required to understand and respond to a human's natural language commands and questions. If a person requests an assistant robot to take me to Alice's office, the robot must know that Alice is a person who owns some unique office, and that take me means it should navigate there. Similarly, if a person requests bring me the heavy, green mug, the robot must have accurate mental models of the physical concepts heavy, green, and mug. To avoid forcing humans to use key phrases or words robots already know, this thesis focuses on helping robots understanding new language constructs through interactions with humans and with the world around them. To understand a command in natural language, a robot must first convert that command to an internal representation that it can reason with. Semantic parsing is a method for performing this conversion, and the target representation is often semantic forms represented as predicate logic with lambda calculus. Traditional semantic parsing relies on hand-crafted resources from a human expert: an ontology of concepts, a lexicon connecting language to those concepts, and training examples of language with abstract meanings. One thrust of this thesis is to perform semantic parsing with sparse initial data. We use the conversations between a robot and human users to induce pairs of natural language utterances with the target semantic forms a robot discovers through its questions, reducing the annotation effort of creating training examples for parsing. We use this data to build more dialog-capable robots in new domains with much less expert human effort (Thomason et al., 2015; Padmakumar et al., 2017). Meanings of many language concepts are bound to the physical world. Understanding object properties and categories, such as heavy, green, and mug requires interacting with and perceiving the physical world. Embodied robots can use manipulation capabilities, such as pushing, picking up, and dropping objects to gather sensory data about them. This data can be used to understand non-visual concepts like heavy and empty (e.g. get the empty carton of milk from the fridge), and assist with concepts that have both visual and non-visual expression (e.g. tall things look big and also exert force sooner than short things when pressed down on). A second thrust of this thesis focuses on strategies for learning these concepts using multi-modal sensory information. We use human-in-the-loop learning to get labels between concept words and actual objects in the environment (Thomason et al., 2016, 2017). We also explore ways to tease out polysemy and synonymy in concept words (Thomason and Mooney, 2017) such as light, which can refer to a weight or a color, the latter sense being synonymous with pale. Additionally, pushing, picking up, and dropping objects to gather sensory information is prohibitively time-consuming, so we investigate strategies for using linguistic information and human input to expedite exploration when learning a new concept (Thomason et al., 2018). Finally, we build an integrated agent with both parsing and perception capabilities that learns from conversations with users to improve both components over time. We demonstrate that parser learning from conversations (Thomason et al., 2015) can be combined with multi-modal perception (Thomason et al., 2016) using predicate-object labels gathered through opportunistic active learning (Thomason et al., 2017) during those conversations to improve performance for understanding natural language commands from humans. Human users also qualitatively rate this integrated learning agent as more usable after it has improved from conversation-based learning.Computer Science

Cognitive Reasoning for Compliant Robot Manipulation

Physically compliant contact is a major element for many tasks in everyday environments. A universal service robot that is utilized to collect leaves in a park, polish a workpiece, or clean solar panels requires the cognition and manipulation capabilities to facilitate such compliant interaction. Evolution equipped humans with advanced mental abilities to envision physical contact situations and their resulting outcome, dexterous motor skills to perform the actions accordingly, as well as a sense of quality to rate the outcome of the task. In order to achieve human-like performance, a robot must provide the necessary methods to represent, plan, execute, and interpret compliant manipulation tasks. This dissertation covers those four steps of reasoning in the concept of intelligent physical compliance. The contributions advance the capabilities of service robots by combining artificial intelligence reasoning methods and control strategies for compliant manipulation. A classification of manipulation tasks is conducted to identify the central research questions of the addressed topic. Novel representations are derived to describe the properties of physical interaction. Special attention is given to wiping tasks which are predominant in everyday environments. It is investigated how symbolic task descriptions can be translated into meaningful robot commands. A particle distribution model is used to plan goal-oriented wiping actions and predict the quality according to the anticipated result. The planned tool motions are converted into the joint space of the humanoid robot Rollin' Justin to perform the tasks in the real world. In order to execute the motions in a physically compliant fashion, a hierarchical whole-body impedance controller is integrated into the framework. The controller is automatically parameterized with respect to the requirements of the particular task. Haptic feedback is utilized to infer contact and interpret the performance semantically. Finally, the robot is able to compensate for possible disturbances as it plans additional recovery motions while effectively closing the cognitive control loop. Among others, the developed concept is applied in an actual space robotics mission, in which an astronaut aboard the International Space Station (ISS) commands Rollin' Justin to maintain a Martian solar panel farm in a mock-up environment. This application demonstrates the far-reaching impact of the proposed approach and the associated opportunities that emerge with the availability of cognition-enabled service robots

The Effect of Augmented Reality Treatment on Learning, Cognitive Load, and Spatial Visualization Abilities

This study investigated the effects of Augmented Reality (AR) on learning, cognitive load and spatial abilities. More specifically, it measured learning gains, perceived cognitive load, and the role spatial abilities play with students engaged in an astronomy lesson about lunar phases. Research participants were 182 students from a public university in southeastern United States, and were recruited from psychology research pool. Participants were randomly assigned to two groups: (a) Augmented Reality and Text Astronomy Treatment (ARTAT); and (b) Images and Text Astronomy Treatment (ITAT). Upon entering the experimental classroom, participants were given (a) Paper Folding Test to measure their spatial abilities; (b) the Lunar Phases Concept Inventory (LPCI) pre-test; (c) lesson on Lunar Phases; (d) NASA-TLX to measure participants’ cognitive load; and (e) LPCI post-test. Statistical analysis found (a) no statistical difference for learning gains between the ARTAT and ITAT groups; (b) statistically significant difference for cognitive load; and (c) no significant difference for spatial abilities scores

Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic, and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.Peer reviewe

Sensorimotor interfaces : towards enactivity in HCI

This thesis explores the application of enactive techniques to human computer interaction, focusing on how devices following ‘sensorimotor’ principles can be blended with interface goals to lead to new perceptual experiences. Building sensorimotor interfaces is an exciting, emerging field of research facing challenges surrounding application, design, training and uptake. To tackle these challenges, this thesis cuts a line of investigation from a review of enactivity in the related field of sensory substitution and augmentation devices, to a schematic taxonomy, model and design guide of ‘the sensorimotor interface’; developed from a theoretically-grounded, enactive approach to cognition. Device, interaction and training guidelines are drawn from this model, formalising the application of the enactive approach to HCI. A readily-available consumer device is then characterised and calibrated in preparation for testing the model validity and associated insights. The process highlights the effects of accessible, easily-implemented calibrations, and the importance of mixed-method approaches in assessing sensorimotor interface potential. The calibrated device is utilised to conduct a detailed, methodological investigation into how concurrently available sensory information affects and contributes to uptake of novel sensorimotor skills. Robust statistical modelling concludes that sensory concurrency has a profound effect on the comprehension and integration of enactive haptic signals, and that efforts to carefully control the nature and degree of sensory concurrency improve user comprehension and enjoyability when engaging with novel sensorimotor tasks, while reducing confusion and stress. The work is concluded by speculation on how the presented derivations, methods and observations can be used to directly influence future sensorimotor interface design in HCI. This thesis therefore constitutes a primer to the principles and history of sensory substitution and augmentation, details the requirements and limitations of the enactive approach in academia and industry, and brings enactivity forward as an accessible, viable and exciting methodology in interaction design