Collaborative Robotics & Adaptive Machines Lab

Discover Our Focus

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The overarching theme of research at the Collaborative Robotics and Adaptive Machines Laboratory is to investigate the role of collaboration in robotics. Ideas are borrowed from diverse fields like bio-inspired robotics, swarm intelligence, developmental psychology, cognitive robotics, and human-robot collaboration. Our goals lie in combining new insights from these fields to design collaborative robots that cater to strategic areas—manufacturing and assistive robotics—of national interest. Our long-term scientific goals lie in using the results of such interdisciplinary research to understand the mechanisms of embodied cognition at closer resolutions.

Our research interests are:

  • Collaborative Robotics
  • Bio-inspired Robotics
  • Social Robotics
  • Swarm Intelligence
  • Embodied Cognition

Explore Our Research

  • Book published in the area of swarm intelligence-based optimization. K. N. Kaipa and D. Ghose. Glowworm Swarm Optimization: Theory, Algorithms, and Applications, Studies in Computational Intelligence, Vol. 698, Springer-Verlag, 2017. ISBN: 978-3-319-51594-6 (Print) 978-3-319-51595-3 (Online).
  • Best Paper Award. ASME Computer-Aided Product and Process Development Technical Committee's Prakash Krishnaswamy Best Paper Award, ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE), Cleveland, Ohio, USA, 2017.
  • Outstanding Teaching Award. For the graduate course, Planning for Autonomous Robots, taught at the University of Maryland in spring 2016.

  1. K. N. Kaipa, A. S. Kankanhalli-Nagendra, N. B. Kumbla, S. Shriyam, S. S. Thevendria-Karthic, J. A. Marvel, and S. K. Gupta (2016). Addressing perception uncertainty induced failure modes in robotic bin-picking. Robotics and Computer Integrated Manufacturing 42(1), 17-38.
  2. C. W. Morato, K. N. Kaipa, and S. K. Gupta (2014). Toward safe human-robot collaboration by using multiple Kinects based real-time human tracking. ASME Journal of Computing and Information Science in Engineering, 14(1): 011006.
  3. C. W. Morato, K. N. Kaipa, and S. K. Gupta (2013). Improving assembly precedence constraint generation by utilizing motion planning and part interaction clusters, Computer-Aided Design, 45 (11): 1349-1364.
  4. K. N. Kaipa, J. C. Bongard, and A. N. Meltzoff (2010). Self-discovery enables robot social cognition: Are you my teacher? Neural Networks, Special Issue on Social Cognition: Babies to Robots, 23(8-9): 1113-1124.
  5. K. N. Kaipa and D. Ghose (2009). Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intelligence, 3(2): 87-124.

Professor & Assistant Dean Engineering Technology
Professor & Chair Engineering Technology
Assoc Dean For Diversity, Equity, Inclusion & Access Engineering & Technology
Master Lecturer Department of Teaching & Learning
Associate Professor Mechanical & Aerospace Engineering
Associate Professor Mechanical & Aerospace Engineering

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"It was very fun," Takhvar said. "I built a robot that did not walk very well, to be honest, but I learned that engineering is more than just one discipline. It's a combination of multiple skills from different fields that you need to utilize to complete a job." - Navy Veteran Davis Takhvar

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