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Human posture prediction during physical human-robot interaction

Abstract : When a human is interacting physically with a robot to accomplish a task, his/her posture is inevitably influenced by the robot movement. Since the human is not controllable, an active robot imposing a collaborative trajectory should predict the most likely human posture. This prediction should consider individual differences and preferences of movement execution, and it is necessary to evaluate the impact of the robot's action from an ergonomics standpoint. Here, we propose a method to predict, in probabilistic terms, the human postures of an individual for a given robot trajectory executed in a collaborative scenario. We formalize the problem as the prediction of the human joints velocity given the current posture and robot end-effector velocity. The key idea of our approach is to learn the distribution of the null space of the Jacobian and the weights of the weighted pseudo-inverse from demonstrated human movements: both carry information about human postural preferences, to leverage redundancy and ensure that the predicted posture will be coherent with the end-effector position. We validate our approach in a simulated toy problem and on two real human-robot interaction experiments where a human is physically interacting with a Franka robot.
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https://hal.archives-ouvertes.fr/hal-03451961
Contributor : Lorenzo Vianello Connect in order to contact the contributor
Submitted on : Friday, November 26, 2021 - 4:56:21 PM
Last modification on : Friday, December 3, 2021 - 3:01:08 AM

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  • HAL Id : hal-03451961, version 1

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Lorenzo Vianello, Jean-Baptiste Mouret, Eloise Dalin, Alexis Aubry, Serena Ivaldi. Human posture prediction during physical human-robot interaction. International Conference on Humanoid Robots, HUMANOIDS 2020, Jul 2021, Munich (Virtual), Germany. ⟨hal-03451961⟩

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