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Creators/Authors contains: "Gesel, Paul"

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  1. ; ; (, 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems)
    In this paper, we propose a novel network architecture for visual imitation learning that exploits neural radiance fields (NeRFs) and key-point correspondence for self-supervised visual motor policy learning. The proposed network architecture incorporates a dynamic system output layer for policy learning. Combining the stability and goal adaption properties of dynamic systems with the robustness of keypoint-based correspondence yields a policy that is invariant to significant clutter, occlusions, lighting conditions changes, and spatial variations in goal configurations. Experiments on multiple manipulation tasks show that our method outperforms comparable visual motor policy learning methods on both in-distribution and out-of-distribution scenarios when using a small number of training samples. 
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  2. ; (, International Conference on Robotics and Automation (ICRA))
    Full Text Available 
  3. ; ; ; (, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))
    Full Text Available 
  4. ; ; ; ; ; (, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))
    Full Text Available 
  5. ; ; ; ; (, Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems)
    null (Ed.)
    Thispaperproposesadynamicsystembased learningfromdemonstrationapproachtoteacharobotac- tivitiesofdailyliving.Theapproachtakesinspirationfrom human movementliteraturetoformulatetrajectorylearningas an optimalcontrolproblem.Weassumeaweightedcombination of basisobjectivefunctionsisthetrueobjectivefunctionfor a demonstratedmotion.Wederivebasisobjectivefunctions analogous tothoseinhumanmovementliteraturetooptimize the robot’smotion.Thismethodaimstonaturallyadapt the learnedmotionindifferentsituations.Tovalidateour approach,welearnmotionsfromtwocategories:1)commonly prescribedtherapeuticexercisesand2)teamaking.Weshow the reproductionaccuracyofourmethodandcomparetorque requirementstothedynamicmotionprimitiveforeachmotion, with andwithoutanaddedload. 
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    Accepted Manuscript Full Text Available
  6. ; ; ; ; (, Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems)
    null (Ed.)
    This paper proposes a dynamic system based learning from demonstration approach to teach a robot activities of daily living. The approach takes inspiration from human movement literature to formulate trajectory learning as an optimal control problem.We assume a weighted combination of basis objective functions is the true objective function for a demonstrated motion. We derive basis objective functions analogous to those in human movement literature to optimize the robot’s motion. This method aims to naturally adapt the learned motion in different situations. To validate our approach, we learn motions from two categories: 1) commonly prescribed therapeutic exercises and 2) tea making. We show the reproduction accuracy of our method and compare torque requirements to the dynamic motion primitive for each motion, with and without an added load. 
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    Accepted Manuscript Full Text Available
  7. ; ; (, International Conference on Robotics and Automation (ICRA))
    Full Text Available