Abstract
For decades, robots have been able to reliably follow precise trajectories making them ideal tools for assembly lines and other structured environments. However, pre-programmed motions fail under uncertainty and are unsafe around humans, making them inadequate for unstructured environments. This paper presents a framework to generate safe, robust, and generalizable robot behaviors for contact tasks where compliance plays a key role. First, we collect task data from haptic demonstrations. Then, we segment the data into a sequence of compliant primitives. Finally, we extract the key parameters required for a robot to perform each of the primitive actions using interpretable, model-based controllers.
This method was experimentally validated on a steel bolting task using a 7-DOF Franka Panda robot. By recombining the primitives, we were also able to screw a cap onto containers of different sizes, placed in arbitrary configurations, using two different 7-DOF manipulators. The results show that our method generates position and orientation invariant, robot-agnostic controllers.
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Acknowledgements
Thank you to everyone at the Stanford Robotics Lab! Special thanks as well to Toki Migimatsu for his invaluable help with the initial exploration of this work. Thank you also to Mikael for his guidance and to Marco Speziali for his help with rendering and grasping advice.
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Herrero, E.G., Piedra, A., Brosque, C., Khatib, O. (2022). Parametrization of Compliant, Object-Level Controllers from Human Demonstrations. In: Altuzarra, O., Kecskeméthy, A. (eds) Advances in Robot Kinematics 2022. ARK 2022. Springer Proceedings in Advanced Robotics, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-031-08140-8_42
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