Bio


Monroe Kennedy III is an Assistant Professor of Mechanical Engineering, with a courtesy appointment in Computer Science. He is the recipient of the NSF Career Award. He received his Ph.D. in Mechanical Engineering and Applied Mechanics, and a Masters in Robotics from the University of Pennsylvania where he was a recipient of both the NSF and GEM graduate research fellowships. His area of expertise is in collaborative robotics, specifically the development of theoretical and experimental approaches to enhance robotic autonomy and robotic effectiveness in decentralized tasks toward human-robot collaboration. He applies expertise in machine learning, computer vision, collaborative robot teammate intent estimation, dynamical systems analysis, control theory (classical, non-linear, and robust control), state estimation and prediction, and motion planning.

He is the director of the Assistive Robotics and Manipulation Lab (ARMLab) whose broad research objective is to develop technology that improves everyday life by anticipating and acting on the needs of human counterparts. ARMLab specializes in developing intelligent robotic systems that can perceive and model environments, humans, and tasks and leverage these models to predict system processes and understand their assistive role. The research can be divided into the following sub-categories: robotic assistants, connected devices, and intelligent wearables. ARMLab research requires the use of a combination of tools in dynamical systems analysis, control theory (classical, non-linear, and robust control), state estimation and prediction, motion planning, vision for robotic autonomy, teammate intent estimation, and machine learning. ARMLab focuses heavily on both the analytical and experimental components of collaborative robotics. Research applications include autonomous assistive technology, robotic assistants (mobile manipulators and humanoids) with the goal of deployment for service tasks that may be highly dynamic and require dexterity, situational awareness, and human-robot collaboration.

Honors & Awards


  • Faculty Early Career Award, National Science Foundation (February 24, 2022)
  • Graduate Research Fellowship, National Science Foundation (2013-2018)

Boards, Advisory Committees, Professional Organizations


  • Member, American Society of Mechanical Engineers (2015 - Present)
  • Member, Institute of Electrical and Electronics Engineers (2016 - Present)

Program Affiliations


Professional Education


  • PhD, University of Pennsylvania, Mechanical Engineering and Applied Mechanics (2019)
  • MS, University of Pennsylvania, Robotics (2016)
  • BS, University of Maryland, Baltimore County, Mechanical Engineering (2012)

Community and International Work


  • National Director for Black in Robotics

    Topic

    Increasing engagement of underrepresented minorities in robotics

    Partnering Organization(s)

    Black in Robotics

    Populations Served

    High school, college and academic/industry professionals

    Location

    International

    Ongoing Project

    Yes

    Opportunities for Student Involvement

    No

Current Research and Scholarly Interests


My research is to develop technology that improves everyday life by anticipating and acting on the needs of human counterparts. The research can be divided into the following sub-categories: robotic assistants, connected devices and intelligent wearables. I use a combination of tools in dynamical systems analysis, control theory (classical, non-linear and robust control), state estimation and prediction, motion planning, vision for robotic autonomy and machine learning. My Assistive Robotics and Manipulation lab (arm.stanford.edu) focuses heavily on both the analytical and experimental components of assistive technology design. While our application area domain is autonomous assistive technology, our primary focus is robotic assistants (mobile manipulators and humanoids) with the goal of deployment for service tasks that may be highly dynamic and require dexterity, situational awareness, and human-robot collaboration.

2024-25 Courses


Stanford Advisees


All Publications


  • Inter-finger Small Object Manipulation With DenseTact Optical Tactile Sensor IEEE ROBOTICS AND AUTOMATION LETTERS Do, W., Aumann, B., Chungyoun, C., Kennedy, M. 2024; 9 (1): 515-522
  • The role of collaborative robotics in assistive and rehabilitation applications. Science robotics Kennedy, M. 2023; 8 (83): eadk6743

    Abstract

    Collaborative robotics principles and advancements may transform the field of assistive and rehabilitation robotics.

    View details for DOI 10.1126/scirobotics.adk6743

    View details for PubMedID 37878691

  • Trajectory and Sway Prediction Towards Fall Prevention. IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation Wang, W., Raitor, M., Collins, S., Liu, C. K., Kennedy, M. 2023; 2023: 10483-10489

    Abstract

    Falls are the leading cause of fatal and non-fatal injuries, particularly for older persons. Imbalance can result from the body's internal causes (illness), or external causes (active or passive perturbation). Active perturbation results from applying an external force to a person, while passive perturbation results from human motion interacting with a static obstacle. This work proposes a metric that allows for the monitoring of the persons torso and its correlation to active and passive perturbations. We show that large changes in the torso sway can be strongly correlated to active perturbations. We also show that we can reasonably predict the future path and expected change in torso sway by conditioning the expected path and torso sway on the past trajectory, torso motion, and the surrounding scene. This could have direct future applications to fall prevention. Results demonstrate that the torso sway is strongly correlated with perturbations. And our model is able to make use of the visual cues presented in the panorama and condition the prediction accordingly.

    View details for DOI 10.1109/icra48891.2023.10161361

    View details for PubMedID 38009123

    View details for PubMedCentralID PMC10671274

  • Diffusion Co-Policy for Synergistic Human-Robot Collaborative Tasks IEEE Robotics and Automation Letters Ng, E., Liu, Z., Kennedy, M. 2023: 1-8

    View details for DOI 10.1109/LRA.2023.3330663

  • Trajectory and Sway Prediction Towards Fall Prevention Wang, W., Raitor, M., Collins, S., Liu, C., Kennedy, M. 2023: 10483-10489
  • It Takes Two: Learning to Plan for Human-Robot Cooperative Carrying Ng, E., Liu, Z., Kennedy, M. 2023: 7526-7532
  • DenseTact 2.0: Optical Tactile Sensor for Shape and Force Reconstruction Do, W., Jurewicz, B., Kennedy, M. 2023: 12549-12555
  • DenseTact: Optical Tactile Sensor for Dense Shape Reconstruction Do, W., Kennedy, M. 2022: 6188-6194
  • Replay Overshooting: Learning Stochastic Latent Dynamics with the Extended Kalman Filter IEEE International Conference on Robotics and Automation Li, A. H., Wu, P., Kennedy, M. 2021: 852-858
  • Considerations for the Control Design of Augmentative Robots Guptasarma, S., Kennedy, M. IEEE IROS Workshop on Building and Evaluating Ethical Robotic Systems. 2021
  • Robots are not immune to bias and injustice. Science robotics Howard, A., Kennedy, M. 3. 2020; 5 (48)

    Abstract

    Human-human social constructs drive human-robot interactions; robotics is thus intertwined with issues surrounding inequity and racial injustices.

    View details for DOI 10.1126/scirobotics.abf1364

    View details for PubMedID 33208524

  • Recent Development in Human Motion and Gait Prediction Zhang, J., Kennedy, M. RSS 2020 Workshop RobRetro. 2020
  • Autonomous Precision Pouring From Unknown Containers IEEE ROBOTICS AND AUTOMATION LETTERS Kennedy, M., Schmeckpeper, K., Thakur, D., Jiang, C., Kumar, V., Daniilidis, K. 2019; 4 (3): 2317–24
  • Modeling And Control For Robotic Assistants: Single And Multi-Robot Manipulation Kennedy, M. D. Publicly Accessible Penn Dissertations. 2019 (3299):
  • Optimal Paths for Polygonal Robots in SE(2) Kennedy, M., Thakur, D., Hsieh, M., Bhattacharya, S., Kumar, V. ASME. 2018

    View details for DOI 10.1115/1.4038980

    View details for Web of Science ID 000426985200006

  • Object Picking Through In-Hand Manipulation Using Passive End-Effectors With Zero Mobility IEEE ROBOTICS AND AUTOMATION LETTERS Mucchiani, C., Kennedy, M., Yim, M., Seo, J. 2018; 3 (2): 1096–1103
  • Precise dispensing of liquids using visual feedback IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Kennedy, M. D., Queen, K., Thakur, D., Daniilidis, K., Kumar, V. 2017
  • Precise Dispensing of Liquids Using Visual Feedback Kennedy, M., Queen, K., Thakur, D., Daniilidis, K., Kumar, V., Bicchi, A., Okamura, A. IEEE. 2017: 1260–66
  • A Triangle Histogram for Object Classification by Tactile Sensing Zhang, M. M., Kennedy, M. D., Hsieh, M., Daniilidis, K., IEEE IEEE. 2016: 4931–38
  • DECENTRALIZED ALGORITHM FOR FORCE DISTRIBUTION WITH APPLICATIONS TO COOPERATIVE TRANSPORT Kennedy, M. D., Guerrero, L., Kumar, V., ASME AMER SOC MECHANICAL ENGINEERS. 2016
  • Decentralized Algorithm for Force Distribution With Applications to Cooperative Transport International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Kennedy III , M., Guerrero, L., Kumar, V. 2015

    View details for DOI 10.1115/DETC2015-47752

  • Automated biomanipulation of single cells using magnetic microrobots INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Steager, E. B., Sakar, M., Magee, C., Kennedy, M., Cowley, A., Kumar, V. 2013; 32 (3): 346–59