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Creators/Authors contains: "Burdick, Joel W"

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  1. ; ; ; (, Proceedings of Machine Learning Research)
    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; (, Proceedings of The 5th Annual Learning for Dynamics and Control Conference)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ; ; ; (, IEEE International Conference on Robotics and Automation (ICRA 2021))
    Full Text Available 
  4. ; ; ; ; (, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))
    Hybrid locomotion, which combines multiple modalities of locomotion within a single robot, enables robots to carry out complex tasks in diverse environments. This paper presents a novel method for planning multi-modal locomotion trajectories using approximate dynamic programming. We formulate this problem as a shortest-path search through a state-space graph, where the edge cost is assigned as optimal transport cost along each segment. This cost is approximated from batches of offline trajectory optimizations, which allows the complex effects of vehicle under-actuation and dynamic constraints to be approximately captured in a tractable way. Our method is illustrated on a hybrid double-integrator, an amphibious robot, and a flying-driving drone, showing the practicality of the approach. 
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    Accepted Manuscript Full Text Available
  5. ; ; ; ; (, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))
    null (Ed.)
    Hybrid locomotion, which combines multiple modalities of locomotion within a single robot, enables robots to carry out complex tasks in diverse environments. This paper presents a novel method for planning multi-modal locomotion trajectories using approximate dynamic programming. We formulate this problem as a shortest-path search through a state-space graph, where the edge cost is assigned as optimal transport cost along each segment. This cost is approximated from batches of offline trajectory optimizations, which allows the complex effects of vehicle under-actuation and dynamic constraints to be approximately captured in a tractable way. Our method is illustrated on a hybrid double-integrator, an amphibious robot, and a flying-driving drone, showing the practicality of the approach. 
    more » « less
    Full Text Available 
  6. ; ; ; ; ; ; (, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))
    null (Ed.)
    Full Text Available 
  7. ; ; ; ; ; (, Thirty-sixth International Conference on Machine Learning)
    null (Ed.)
    Accepted Manuscript Full Text Available