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END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20320314T030000 RDATE:20321107T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20330313T030000 RDATE:20331106T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20340312T030000 RDATE:20341105T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20350311T030000 RDATE:20351104T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20360309T030000 RDATE:20361102T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20370308T030000 RDATE:20371101T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:b3e233f0ddb0066c3349e714769fa5b0 CATEGORIES:RuCCS Colloquia:Fall 2018 CREATED:20180621T183948 SUMMARY:"Human-Robot Interactive Control using Brain and Muscle Interfaces", Peter Allen (CS, Columbia) - Hosted by Kostas Bekris LOCATION:Busch Campus\, Psych 101 DESCRIPTION:Peter K. Allen, Department of Computer Science, Columbia University\nhttp:/ /www.cs.columbia.edu/~allen\n \nAbstract: As robotic systems begin to popul ate our daily life, Human-Robot interaction modes and control become increa singly important. Besides the traditional methods of interacting with a rob ot such as speech, gesture, or keyboard/joystick, Brain and Muscle Computer Interfaces (BMCI) can also be used. These interfaces provide an intriguing method for controlling remote agents. Human operators can easily become ov erloaded as task responsibilities increase. By using a BMCI, possibly in co ncert with other interfaces, a human operator can begin multi-task control of multiple agents, some controlled by more traditional interfaces, and oth ers using a BMCI. This work also finds high applicability in the Assistive Robotics space, where it can assist individuals with limited speech, mobili ty and motion control. In this talk, we present three systems we have built that utilize these interfaces. The first is an assistive robotic grasping system that allows impaired individuals to interact with the system in a hu man-in-the-loop manner, including the use of a novel cranio-facial electrom yography input device. The system uses an augmented reality interface that allows users to plan grasps online that match their task-oriented intents. The second is a shared control online grasp planner. It uses an EEG-based i nterface to recognize the user’s grasping preferences. The EEG signal class ifier is fast and simple to train, and the system as a whole requires almos t no learning on the part of the subject. The last system is a novel hierar chical system for shared control of a humanoid robot. Our framework uses a Brain Computer Interface (BCI) to interpret EEG signals via Steady- State V isual Evoked Potentials (SSVEP). Our system leverages the ability of the ro bot to accomplish low level tasks on its own, while the user assists the ro bot with high level directions when needed.\n \nBiosketch: Peter Allen is P rofessor of Computer Science at Columbia University, and Director of the Co lumbia Robotics Lab. He is the recipient of the CBS Foundation Fellowship, Army Research Office fellowship, the Rubinoff Award for innovative uses of computers, and the NSF PYI award. His current research interests include ro botic grasping, medical robotics and Brain-Computer Interfaces for Human-Ro bot interaction\n \nSuggested Reading:\n\n - Grasp ing with Your Brain: A Brain-Computer Interface for Fast Grasp Selection by Robert Ying, Jonathan Weisz and Peter K. Allen, Robotics Research: Volume 1, eds. A. Bicchi and W. Burgard, pp. 325-340, 2018, Springer\n - Task Level Hierarchical System for BCI-enabled Shared Autonomy by Iretiayo Akino la, Boyuan Chen, Jonathan Koss, Aalhad Patankar, Jake Varley and Peter Alle n, IEEE/RAS International Conference on Humanoid Robotics, Nov. 2017 (http: //www.cs.columbia.edu/~allen/PAPERS/Humanoids_2017.pdf) The RuCCS Coll oquia Series is organized by Dr. Julien Musolino and Dr. Sara Pixley. The t alks are held on Tuesdays in the Psychology Building , Room 101 on the Busch Campus (http://maps.rutgers.edu/maps/default.aspx?c ampus=4?300,0) from 1:00-2:30pm.\nNote: If you would like to receive email announcements about the colloquium series, please contact the Business Offi ce to have your name added to our announce lists at This email address is b eing protected from spambots. You need JavaScript enabled to view it..\n X-ALT-DESC;FMTTYPE=text/html:
Peter K. Allen, Department of Computer Science, Columbia Un
iversity
http://www.cs.columbia.edu/~allen
p>
Abstract: As robotic systems begin to populate our da ily life, Human-Robot interaction modes and control become increasingly imp ortant. Besides the traditional methods of interacting with a robot such as speech, gesture, or keyboard/joystick, Brain and Muscle Computer Interface s (BMCI) can also be used. These interfaces provide an intriguing method fo r controlling remote agents. Human operators can easily become overloaded a s task responsibilities increase. By using a BMCI, possibly in concert with other interfaces, a human operator can begin multi-task control of multipl e agents, some controlled by more traditional interfaces, and others using a BMCI. This work also finds high applicability in the Assistive Robotics s pace, where it can assist individuals with limited speech, mobility and mot ion control. In this talk, we present three systems we have built that util ize these interfaces. The first is an assistive robotic grasping system tha t allows impaired individuals to interact with the system in a human-in-the -loop manner, including the use of a novel cranio-facial electromyography i nput device. The system uses an augmented reality interface that allows use rs to plan grasps online that match their task-oriented intents. The second is a shared control online grasp planner. It uses an EEG-based interface t o recognize the user’s grasping preferences. The EEG signal classifier is f ast and simple to train, and the system as a whole requires almost no learn ing on the part of the subject. The last system is a novel hierarchical sys tem for shared control of a humanoid robot. Our framework uses a Brain Comp uter Interface (BCI) to interpret EEG signals via Steady- State Visual Evok ed Potentials (SSVEP). Our system leverages the ability of the robot to acc omplish low level tasks on its own, while the user assists the robot with h igh level directions when needed.
Biosketch: Peter Allen is Professor of Computer Science at Columbia University, and Director of the Columbia Robotics Lab. He is the recipient of the CBS F oundation Fellowship, Army Research Office fellowship, the Rubinoff Award f or innovative uses of computers, and the NSF PYI award. His current researc h interests include robotic grasping, medical robotics and Brain-Computer I nterfaces for Human-Robot interaction
Suggested Reading:
The RuCCS Colloquia Series is organized by Dr. Julien Musolino and Dr. Sara Pixley. The talks are held on Tuesdays in the Psychology Buildin g, Room 101 on the Busch Ca mpus from 1:00-2:30pm.
Note: If you woul
d like to receive email announcements about the colloquium series, please c
ontact the Business Office to have your name added to our announce lists at