Adaptive Neural Admittance Control for Collision Avoidance in HumanRobot Collaborative Tasks This paper proposed an adaptive neural admittance control strategy for collision avoidance in humanrobot collaborative tasks. In order to ensure that the robot endeffector can avoid collisions with surroundings, robot should be operated compliantly by human within a constrained task space. An impedance model and a soft saturation function are employed to generate a differentiable reference trajectory. Then, adaptive neural network control with position constraint, based on integral barrier Lyapunov function (IBLF), is designed to achieve precise tracking while guaranteeing constrained satisfaction. Utilizing Lyapunov stability principles, we prove that semiglobally uniformly bounded stability is guaranteed for all states of the closedloop system. At last, the effectiveness of the proposed algorithm is verified on a Baxter robot experimental platform. Collisions with surroundings can be avoided in humanrobot collaborative tasks.

Learning Barrier Functions for Constrained Motion Planning with Dynamical Systems Stable dynamical systems are a flexible tool to plan robotic motions in realtime. In the robotic literature, dynamical system motions are typically planned without considering possible limitations in the robotxe2x80x99s workspace. This work presents a novel approach to learn workspace constraints from human demonstrations and to generate motion trajectories for the robot that lie in the constrained workspace. Training data are incrementally clustered into different linear subspaces and used to fit a low dimensional representation of each subspace. By considering the learned constraint subspaces as zeroing barrier functions, we are able to design a control input that keeps the system trajectory within the learned bounds. This control input is effectively combined with the original system dynamics preserving eventual asymptotic properties of the unconstrained system. Simulations and experiments on a real robot show the effectiveness of the proposed approach.