Robot-Safe Impacts with Soft Contacts Based on Learned Deformations

Niels Dehio (1) , Abderrahmane Kheddar (1)

1 IDH - Interactive Digital Humans

Safely generating impacts with robots is challenging due to subsequent discontinuous velocity and high impact forces. We aim at increasing the impact velocity - the robot's relative speed prior to contact - such that impact-tasks like grabbing and boxing are made with the highest allowable speed performance when needed. Previous works addressed this problem for rigid bodies' impacts. This letter proposes a control paradigm for generating intentional impacts with deformable contacts that incorporates hardware and task constraints. Based on data-driven learning of the shock-absorbing soft dynamics and a novel mapping of joint-space limits to contact-space, we devise a constrained model-predictive control to maximize the intentional impact within a feasible, robot-safe level. Our approach is assessed with real-robot experiments on the redundant Panda manipulator, demonstrating high pre-impact velocities (up to 0.9 m/s) of a rigid end-effector on soft objects and an end-effector soft suction-pump on rigid or deformable objects.

Domaines: Robotique [cs.RO]

Niels Dehio, Abderrahmane Kheddar. Robot-Safe Impacts with Soft Contacts Based on Learned Deformations. ICRA 2021 - 38th IEEE International Conference on Robotics and Automation, May 2021, Xi'an, China. pp.1357-1363, ⟨10.1109/ICRA48506.2021.9561678⟩. ⟨hal-02973947v3⟩