Optimization of sequential attractor-based movement for compact behaviour generation

In this paper, we propose a novel method to generate optimal robot motion based on a sequence of attractor dynamics in task space. This is motivated by the biological evidence that movements in the motor cortex of animals are encoded in a similar fashion- and by the need for compact movement representations on which efficient optimization can be performed. We represent the motion as a sequence of attractor points acting in the task space of the motion. Based on this compact and robust representation, we present a scheme to generate optimal movements. Unlike traditional optimization techniques, this optimization is performed on the low-dimensional representation of the attractor points and includes the underlying control loop itself as subject to optimization. We incorporate optimality criteria such as e.g. the smoothness of the motion, collision distance measures, or joint limit avoidance. The optimization problem is solved efficiently employing the analytic equations of the overall system. Due to the fast convergence, the method is suited for dynamic environments, including the interaction with humans. We will present the details of the optimization scheme, and give a description of the chosen optimization criteria. Simulation and experimental results on the humanoid robot ASIMO will underline the potential of the proposed approach.