10 research outputs found
A neural network-based exploratory learning and motor planning system for co-robots
Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or "learning by doing," an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object
The Unlock Project: A Python-based framework for practical brain-computer interface communication “app” development
In this paper we present a framework for reducing the development time needed for creating applications for use in non-invasive brain-computer interfaces (BCI). Our framework is primarily focused on facilitating rapid software “app” development akin to current efforts in consumer portable computing (e.g. smart phones and tablets). This is accomplished by handling intermodule communication without direct user or developer implementation, instead relying on a core subsystem for communication of standard, internal data formats. We also provide a library of hardware interfaces for common mobile EEG platforms for immediate use in BCI applications. A use-case example is described in which a user with amyotrophic lateral sclerosis participated in an electroencephalography-based BCI protocol developed using the proposed framework. We show that our software environment is capable of running in real-time with updates occurring 50–60 times per second with limited computational overhead (5 ms system lag) while providing accurate data acquisition and signal analysis
Neuromatch Academy: Teaching Computational Neuroscience with global accessibility
Neuromatch Academy designed and ran a fully online 3-week Computational
Neuroscience summer school for 1757 students with 191 teaching assistants
working in virtual inverted (or flipped) classrooms and on small group
projects. Fourteen languages, active community management, and low cost allowed
for an unprecedented level of inclusivity and universal accessibility.Comment: 10 pages, 3 figures. Equal contribution by the executive committee
members of Neuromatch Academy: Tara van Viegen, Athena Akrami, Kate Bonnen,
Eric DeWitt, Alexandre Hyafil, Helena Ledmyr, Grace W. Lindsay, Patrick
Mineault, John D. Murray, Xaq Pitkow, Aina Puce, Madineh Sedigh-Sarvestani,
Carsen Stringer. and equal contribution by the board of directors of
Neuromatch Academy: Gunnar Blohm, Konrad Kording, Paul Schrater, Brad Wyble,
Sean Escola, Megan A. K. Peter
A neural network-based exploratory learning and motor planning system for co-robots
Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or learning by doing, an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object