Optimization of sequential attractor-based movement for compact
Marc Toussaint, Michael Gienger and Christian Goerick
In: 7th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2007), Dec 2007, Pitsburgh.
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.
|EPrint Type:||Conference or Workshop Item (Talk)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Deposited By:||Marc Toussaint|
|Deposited On:||25 February 2008|