The Labs’ perception programs give machines the intelligence to identify people, objects, and scenes. Human analysis allows machines to detect and track human movements, including specific gesture and activity recognition, affect recognition and face recognition. Similarly, our object recognition can detect and track moving objects, as well as classify objects and conduct pose estimation. Vecna’s sensor fusion and world modeling technology also supports autonomous navigation through location recognition, 3D reconstruction, and SLAM.
Dr. Ashwin Thangali
Dr. Thangali has extensive experience related to computer vision (tracking, object detection and recognition) and machine learning . At Vecna, Dr. Thangali leads the Computer Vision group which provides perception capabilities to Vecna’s commercial robotics products. He leads several ongoing government sponsored research programs for Army, NASA and NAVAIR. These projects include human activitiy analysis, object tracking, event detection and classification. Dr. Thangali has a BE from the National Institute of Technology, Surathkal, ME from the Indian Institute of Science, Bangalore and a Ph.D. from Boston University where he wrote his thesis on algorithms for American Sign Language (ASL) recognition from video input.
Vecna’s navigation system gives robots the ability to safely and efficiently negotiate unstructured environments. The Labs team has developed a context-aware navigation system that uses sensor fusion and world modeling technology to plan paths among dynamic obstacles. In addition, the system supports optimal path planning, person following, autonomous docking to charging stations, and topological planning.
Dr. Zac Dydek
Dr. Dydek is the Director of Autonomous Systems, driving cross-product decision making and creating company vision for Vecna’s robotics software solutions. Dr. Dydek’s graduate research involved the conception, design, and implementation of advanced, nonlinear controllers with applications to manned and unmanned aerial vehicles. He received the National Defense Science and Engineering Graduate Fellowship from the Department of Defense in 2006. Dr. Dydek has a BS degree in mechanical engineering with a minor in control and dynamical systems from Caltech and has MS and PhD degrees in Mechanical Engineering from MIT.
Vecna develops machines dexterous enough to handle small parts and strong enough to lift several hundred pounds. The Labs leverage hydraulics to create arms that are strong and fast while remaining lightweight and energy-efficient. These systems are equipped with advanced autonomy, including autonomous grasp selection, collision-free motion planning, and multi-sensor guided movement making them easy to use and safe around human workers.
Dr. Shawn Schaffert
Dr. Schaffert has a wealth of experience in developing autonomous robotic systems. He has worked on systems ranging from small electromechanical arms and differential drive bases to a car-sized, custom-built hydraulically-actuated arm and a 30 degree-of-freedom bipedal robot. He received the Doctor of Philosophy degree from the Electrical Engineering and Computer Science Department at the University of California – Berkeley in 2006.
Usability is key to advanced technologies. Vecna Labs strives to create intuitive interfaces for machine supervision, including control by mobile and gesture interaction. Vecna’s machines measure human response through passive and automatic methods, including through machine vision and learning. Intention expression also conveys a robot’s internal state and can help guide human interactions.
Dr. Neil Tenenholtz
Dr. Tenenholtz is a Research Scientist at Vecna Technologies investigating the intersection of machine learning, high performance computing, and simulation and their application to human-robot interaction. Previously, Dr. Tenenholtz received his PhD from Harvard University where he was a recipient of the NSF Graduate Research Fellowship, the Link Foundation Advanced Simulation Fellowship, and the Winston Chen Fellowship.
Multiple Agent Coordination
Vecna’s team develops technology that coordinates fleets of autonomous systems. Our system performs multi-agent resource allocation, constrained optimization, task planning and scheduling, route generation, and execution monitoring. It coordinates agents to work independently, freeing operators to oversee groups of robots rather than commanding low-level tasks for each individual unit.
Dr. Frederik Heger
Dr. Heger leads Vecna’s Robot Navigation and Autonomy team. His expertise is in AI for intelligent mission autonomy, fleet management, and robust robot navigation. Dr. Heger is specifically interested in making teams of robots capable and useful to provide real economic value today and in the future. He leads research projects that develop cutting-edge functionality and transition it from the lab into Vecna’s products. Dr. Heger’s graduate research focused on combining task and motion planning to enable teams of robots to reliably perform complex missions. He holds BSE and MSE degrees in mechanical engineering from the University of Pennsylvania, and MS and PhD degrees in Robotics from Carnegie Mellon University.
A core component of Vecna’s research is inventing and productizing novel ways of capturing and working with medical data. Since 2001, Vecna has done innovative research into infection control, medication management, clinical decision support, and clinical scheduling. All of these have been converted into new features and products in Patient Solutions product suite.
Dr. Alvin Ramsey
Alvin Ramsey, PhD, is the Technology Lead for Healthcare Informatics. He has been the technical lead for the DOD Patient Self-service implementation, and his areas of research includes clinical decision support, machine learning, and data mining. With a BS and MS from MIT and a PhD from UC Berkeley, Dr. Ramsey has extensive experience in software development and system architecture. Before joining Vecna, Dr. Ramsey had previous management experience with developing and productizing new technologies for commercialization.
Dr. Chris Larsen
Christopher Larsen Ph.D. is an award winning biochemist and microbiologist (Emory – Ph.D.; Harvard – Post Doc). His professional goal is to organize the life science knowledge space of bacterial and viral pathogens, for the purposes of clinical infection control, microbial pathogen surveillance, diagnostics, and vaccine development. Chris designs informatics algorithms, to devise new principles upon which to order research information meaningfully. He contributes to company community service efforts in charity races and as a robotics team mentor.
Scott Kullberg is a Senior Software Engineer with a SB in Electrical Engineering and Computer Science from MIT. Scott has extensive experience in software architecture, engineering, and development, and system administration. At Vecna Scott has concentrated on the QC Pathfinder infection control software, medical informatics, robot control, and bioinformatics data management.
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