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Mechanical & Aerospace Engineering Department Faculty

Mechanical & Aerospace EngineeringCollaborative Robotics & Adaptive Machines Laboratory

Discover Our focus

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
MAE Group Pic

"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 and incoming ODU freshman Davis Takhvar

Explore Our Research

Bio-Inspired Robotics

Monarchs Reign at Old Dominion University

Bio-Inspired Robotics

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 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.

Anthony Dean

    Professor & Assistant Dean
  • NORFOLK, VA 23529
  • 757-683-7121
  • adean@odu.edu

Vukica Jovanović

Stacie Ringleb

Jennifer Kidd

  • NORFOLK, VA 23529
  • 757-683-3248
  • jkidd@odu.edu

Oleksandr Kravchenko

  • NORFOLK, VA 23529
  • 757-683-5045
  • okravche@odu.edu

Dipankar Ghosh

  • NORFOLK, VA 23529
  • 757-683-3738
  • dghosh@odu.edu

Kuka LBR iiwa Robot

Kuka Robot

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NAO Robot

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