NASA Center for Intelligent Robotic Systems for Space Exploration

NASA's program for the civilian exploration of space is a challenge to scientists and engineers to help maintain and further develop the United States' position of leadership in a focused sphere of space activity. Such an ambitious plan requires the contribution and further development of many scientific and technological fields. One research area essential for the success of these space exploration programs is Intelligent Robotic Systems. These systems represent a class of autonomous and semi-autonomous machines that can perform human-like functions with or without human interaction. They are fundamental for activities too hazardous for humans or too distant or complex for remote telemanipulation. To meet this challenge, Rensselaer Polytechnic Institute (RPI) has established an Engineering Research Center for Intelligent Robotic Systems for Space Exploration (CIRSSE). The Center was created with a five year $5.5 million grant from NASA submitted by a team of the Robotics and Automation Laboratories. The Robotics and Automation Laboratories of RPI are the result of the merger of the Robotics and Automation Laboratory of the Department of Electrical, Computer, and Systems Engineering (ECSE) and the Research Laboratory for Kinematics and Robotic Mechanisms of the Department of Mechanical Engineering, Aeronautical Engineering, and Mechanics (ME,AE,&M), in 1987. This report is an examination of the activities that are centered at CIRSSE

Neural Network Modeling of Sensory-Motor Control in Animals

National Science Foundation (IRI 90-24877, IRI 87-16960); Air Force Office of Scientific Research (F49620-92-J-0499); Office of Naval Research (N00014-92-J-1309

From Parallel Sequence Representations to Calligraphic Control: A Conspiracy of Neural Circuits

Calligraphic writing presents a rich set of challenges to the human movement control system. These challenges include: initial learning, and recall from memory, of prescribed stroke sequences; critical timing of stroke onsets and durations; fine control of grip and contact forces; and letter-form invariance under voluntary size scaling, which entails fine control of stroke direction and amplitude during recruitment and derecruitment of musculoskeletal degrees of freedom. Experimental and computational studies in behavioral neuroscience have made rapid progress toward explaining the learning, planning and contTOl exercised in tasks that share features with calligraphic writing and drawing. This article summarizes computational neuroscience models and related neurobiological data that reveal critical operations spanning from parallel sequence representations to fine force control. Part one addresses stroke sequencing. It treats competitive queuing (CQ) models of sequence representation, performance, learning, and recall. Part two addresses letter size scaling and motor equivalence. It treats cursive handwriting models together with models in which sensory-motor tmnsformations are performed by circuits that learn inverse differential kinematic mappings. Part three addresses fine-grained control of timing and transient forces, by treating circuit models that learn to solve inverse dynamics problems.National Institutes of Health (R01 DC02852

A Vector-Integration-to-Endpoint Model for Performance of Viapoint Movements

Viapoint (VP) movements are movements to a desired point that are constrained to pass through an intermediate point. Studies have shown that VP movements possess properties, such as smooth curvature around the VP, that are not explicable by treating VP movements as strict concatenations of simpler point-to-point (PTP) movements. Such properties have led some theorists to propose whole-trajectory optimization models, which imply that the entire trajectory is pre-computed before movement initiation. This paper reports new experiments conducted to systematically compare VP with PTP trajectories. Analyses revealed a statistically significant early directional deviation in VP movements but no associated curvature change. An explanation of this effect is offered by extending the Vector-Integration-To-Endpoint (VITE) model (Bullock and Grossberg, 1988), which postulates that voluntary movement trajectories emerge as internal gating signals control the integration of continuously computed vector commands based on the evolving, perceptible difference between desired and actual position variables. The model explains the observed trajectories of VP and PTP movements as emergent properties of a dynamical system that does not precompute entire trajectories before movement initiation. The new model includes a working memory and a stage sensitive to time-to-contact information. These cooperate to control serial performance. The structural and functional relationships proposed in the model are consistent with available data on forebrain physiology and anatomy.Office of Naval Research (N00014-92-J-1309, N00014-93-1-1364, N0014-95-1-0409

Multimodal agents for cooperative interaction

2020 Fall.Includes bibliographical references.Embodied virtual agents offer the potential to interact with a computer in a more natural manner, similar to how we interact with other people. To reach this potential requires multimodal interaction, including both speech and gesture. This project builds on earlier work at Colorado State University and Brandeis University on just such a multimodal system, referred to as Diana. I designed and developed a new software architecture to directly address some of the difficulties of the earlier system, particularly with regard to asynchronous communication, e.g., interrupting the agent after it has begun to act. Various other enhancements were made to the agent systems, including the model itself, as well as speech recognition, speech synthesis, motor control, and gaze control. Further refactoring and new code were developed to achieve software engineering goals that are not outwardly visible, but no less important: decoupling, testability, improved networking, and independence from a particular agent model. This work, combined with the effort of others in the lab, has produced a "version 2'' Diana system that is well positioned to serve the lab's research needs in the future. In addition, in order to pursue new research opportunities related to developmental and intervention science, a "Faelyn Fox'' agent was developed. This is a different model, with a simplified cognitive architecture, and a system for defining an experimental protocol (for example, a toy-sorting task) based on Unity's visual state machine editor. This version too lays a solid foundation for future research

Developmental Learning for Autonomous Robots

Developmental robotics is concerned with the design of algorithms that promote robot adaptation and learning through qualitative growth of behaviour and increasing levels of competence. This paper uses ideas and inspiration from early infant psychology (up to 3 months of age) to examine how robot systems could discover the structure of their local sensory-motor spaces and learn how to coordinate these for the control of action. An experimental learning model is described and results from robotic experiments using the model are presented and discussed

Simulating sensorimotor systems with cortical topology

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references.Not availabl

Building Blocks for Cognitive Robots: Embodied Simulation and Schemata in a Cognitive Architecture

Hemion N. Building Blocks for Cognitive Robots: Embodied Simulation and Schemata in a Cognitive Architecture. Bielefeld: Bielefeld University; 2013.Building robots with the ability to perform general intelligent action is a primary goal of artificial intelligence research. The traditional approach is to study and model fragments of cognition separately, with the hope that it will somehow be possible to integrate the specialist solutions into a functioning whole. However, while individual specialist systems demonstrate proficiency in their respective niche, current integrated systems remain clumsy in their performance. Recent findings in neurobiology and psychology demonstrate that many regions of the brain are involved not only in one but in a variety of cognitive tasks, suggesting that the cognitive architecture of the brain uses generic computations in a distributed network, instead of specialist computations in local modules. Designing the cognitive architecture for a robot based on these findings could lead to more capable integrated systems. In this thesis, theoretical background on the concept of embodied cognition is provided, and fundamental mechanisms of cognition are discussed that are hypothesized across theories. Based on this background, a view of how to connect elements of the different theories is proposed, providing enough detail to allow computational modeling. The view proposes a network of generic building blocks to be the central component of a cognitive architecture. Each building block learns an internal model for its inputs. Given partial inputs or cues, the building blocks can collaboratively restore missing components, providing the basis for embodied simulation, which in theories of embodied cognition is hypothesized to be a central mechanism of cognition and the basis for many cognitive functions. In simulation experiments, it is demonstrated how the building blocks can be autonomously learned by a robot from its sensorimotor experience, and that the mechanism of embodied simulation allows the robot to solve multiple tasks simultaneously. In summary, this thesis investigates how to develop cognitive robots under the paradigm of embodied cognition. It provides a description of a novel cognitive architecture and thoroughly discusses its relation to a broad body of interdisciplinary literature on embodied cognition. This thesis hence promotes the view that the cognitive system houses a network of active elements, which organize the agent's experiences and collaboratively carry out many cognitive functions. On the long run, it will be inevitable to study complete cognitive systems such as the cognitive architecture described in this thesis, instead of only studying small learning systems separately, to answer the question of how to build truly autonomous cognitive robots