Generating and Customizing Robotic Arm Trajectories using Neural Networks

TL;DR

This paper presents a neural network-based method for generating and customizing precise robotic arm trajectories, demonstrated on a cognitive robotics platform, enhancing movement accuracy and adaptability.

Contribution

The paper introduces a novel neural network approach that computes and generates customizable, precise robotic arm trajectories, integrating forward kinematics with joint angle generation.

Findings

Successfully generated precise, customizable trajectories

Enhanced robot movement predictability during human interaction

Demonstrated broad applicability across different settings

Abstract

We introduce a neural network approach for generating and customizing the trajectory of a robotic arm, that guarantees precision and repeatability. To highlight the potential of this novel method, we describe the design and implementation of the technique and show its application in an experimental setting of cognitive robotics. In this scenario, the NICO robot was characterized by the ability to point to specific points in space with precise linear movements, increasing the predictability of the robotic action during its interaction with humans. To achieve this goal, the neural network computes the forward kinematics of the robot arm. By integrating it with a generator of joint angles, another neural network was developed and trained on an artificial dataset created from suitable start and end poses of the robotic arm. Through the computation of angular velocities, the robot was characterized by its ability to perform the movement, and the quality of its action was evaluated in terms of shape and accuracy. Thanks to its broad applicability, our approach successfully generates precise trajectories that could be customized in their shape and adapted to different settings.