Learning inverse kinematics
نویسندگان
چکیده
Real-time control of the endeffector of a humanoid robot in external coordinates requires computationally efficient solutions of the inverse kinematics problem. In this context, this paper investigates inverse kinematics learning for resolved motion rate control (RMRC) employing an optimization criterion to resolve kinematic redundancies. Our learning approach is based on the key observations that learning an inverse of a non uniquely invertible function can be accomplished by augmenting the input representation to the inverse model and by using a spatially localized learning approach. We apply this strategy to inverse kinematics learning and demonstrate how a recently developed statistical learning algorithm, Locally Weighted Projection Regression, allows efficient learning of inverse kinematic mappings in an incremental fashion even when input spaces become rather high dimensional. The resulting performance of the inverse kinematics is comparable to Liegeois’ [9] analytical pseudo-inverse with optimization. Our results are illustrated with a 30 degree of freedom humanoid robot.
منابع مشابه
Modular Neural Net System for Inverse Kinematics Learning
Inverse kinematics computation using an arti cial neural network that learns the inverse kinematics of a robot arm has been employed by many researchers. However, conventional learning methodologies do not pay enough attention to the discontinuity of the inverse kinematics system of typical robot arms with joint limits. The inverse kinematics system of the robot arms is a multi-valued and disco...
متن کاملInverse kinematics learning by modular architecture neural networks
Inverse kinematics computation using an artificial neural network that learns the inverse kinematics of a robot arm has been employed by many researchers. However, conventional learning methodologies do not p a y enough attention to the discontinuity of the inverse kinematics system of typical robot arms with joint limits. The inverse kinematics system of the robot arms, including a human arm w...
متن کاملLearning Global Direct Inverse Kinematics
We introduce and demonstrate a bootstrap method for construction of an inverse function for the robot kinematic mapping using only sample configuration– space/workspace data. Unsupervised learning (clustering) techniques are used on pre–image neighborhoods in order to learn to partition the configuration space into subsets over which the kinematic mapping is invertible. Supervised learning is t...
متن کاملLearning Inverse Kinematics for Pose-Constraint Bi-manual Movements
We present a neural network approach to learn inverse kinematics of the humanoid robot ASIMO, where we focus on bi-manual tool use. The learning copes with both the highly redundant inverse kinematics of ASIMO and the additional arbitrary constraint imposed by the tool that couples both hands. We show that this complex kinematics can be learned from few ground-truth examples using an efficient ...
متن کامل