2 research outputs found
CycleIK: Neuro-inspired Inverse Kinematics
The paper introduces CycleIK, a neuro-robotic approach that wraps two novel
neuro-inspired methods for the inverse kinematics (IK) task, a Generative
Adversarial Network (GAN), and a Multi-Layer Perceptron architecture. These
methods can be used in a standalone fashion, but we also show how embedding
these into a hybrid neuro-genetic IK pipeline allows for further optimization
via sequential least-squares programming (SLSQP) or a genetic algorithm (GA).
The models are trained and tested on dense datasets that were collected from
random robot configurations of the new Neuro-Inspired COLlaborator (NICOL), a
semi-humanoid robot with two redundant 8-DoF manipulators. We utilize the
weighted multi-objective function from the state-of-the-art BioIK method to
support the training process and our hybrid neuro-genetic architecture. We show
that the neural models can compete with state-of-the-art IK approaches, which
allows for deployment directly to robotic hardware. Additionally, it is shown
that the incorporation of the genetic algorithm improves the precision while
simultaneously reducing the overall runtime.Comment: Accepted at ICANN 2023 (32nd International Conference on Artificial
Neural Networks
NICOL: A Neuro-inspired Collaborative Semi-humanoid Robot that Bridges Social Interaction and Reliable Manipulation
Robotic platforms that can efficiently collaborate with humans in physical
tasks constitute a major goal in robotics. However, many existing robotic
platforms are either designed for social interaction or industrial object
manipulation tasks. The design of collaborative robots seldom emphasizes both
their social interaction and physical collaboration abilities. To bridge this
gap, we present the novel semi-humanoid NICOL, the Neuro-Inspired COLlaborator.
NICOL is a large, newly designed, scaled-up version of its well-evaluated
predecessor, the Neuro-Inspired COmpanion (NICO). NICOL adopts NICO's head and
facial expression display and extends its manipulation abilities in terms of
precision, object size, and workspace size. Our contribution in this paper is
twofold -- firstly, we introduce the design concept for NICOL, and secondly, we
provide an evaluation of NICOL's manipulation abilities by presenting a novel
extension for an end-to-end hybrid neuro-genetic visuomotor learning approach
adapted to NICOL's more complex kinematics. We show that the approach
outperforms the state-of-the-art Inverse Kinematics (IK) solvers KDL, TRACK-IK
and BIO-IK. Overall, this article presents for the first time the humanoid
robot NICOL, and contributes to the integration of social robotics and neural
visuomotor learning for humanoid robots