LBE-DDIK: Is One Model Good Enough to Learn-By-Example the Inverse Kinematics of Multiple Serial Robots?

Jacket Demby's, Ramy Farag, Seyed Mohamad Ali Tousi, Gbenga Omotara, Guilherme N. DeSouza

Abstract:

Data-Driven Inverse Kinematics (DDIK) solvers
emerged as promising Inverse Kinematics (IK) methods for
reliably approximating the IK of robotic manipulators. However, these solvers remain heavily robot-dependent, where for
each robot of interest, a network needs to be trained in an
one-solver-one-robot framework. In this paper, we build on our
previous work on Learning-By-Example (LBE) for DDIK, and
introduce an one-solver-many-robots framework; where a single
neural network is used to predict the IK of multiple robots –
mainly with 6 and 7 Degrees of Freedom (DoF). In our LBE
approach, the neural network input includes an example of
joint-pose tuple (e.g. any previous joint and corresponding pose
tuple in the path) along with the queried pose as the same
network outputs the desired robot joint configuration. Here,
we investigate five network architectures: a Plain Multilayer
Perceptron (MLP), a Residual-based MLP (RMLP), a Densely
Connected MLP (DMLP), and two transformers inspired by
Generative Pre-trained Transformer (GPT) and tested them
using 3 diverse datasets with 20 real-world robotic arms with 6
and 7 DoF. Our experimental results demonstrate that a single
lightweight, LBE-based DDIK solver can reliably predict the IK
for multiple and hitherto unseen robots, within each of the 6 or
7DoF family as well as across both 6 and 7DoF robot families
with position errors below 1mm and orientation errors below
1deg. Additionally, we compare all proposed LBE-DDIK solvers
with three established numerical IK solvers: Selectively Damped
Least-Squares (SD), Singular Value Filtering (SVF), and Mixed
Inverse (MX) and observe that our LBE-DDIK solvers achieve
comparable accuracy, with the advantage of being a one-solvermany-robots framework.