Generating and Customizing Robotic Arm Trajectories using Neural Networks

2025-06-25

AI Summary

To address the challenge of simultaneously achieving flexibility and precision in robotic manipulator trajectory generation, this paper proposes a neural-network-based end-to-end trajectory generation framework. The method unifies forward kinematics modeling and joint-angle sequence prediction within a single neural architecture, trained via supervised learning on a synthetically generated high-quality dataset. To enhance motion smoothness and controllability, angular velocity constraints are explicitly incorporated into the optimization objective. The framework supports user-defined trajectory specifications—such as linear pointing—and exhibits strong adaptability across diverse task scenarios. Experimental evaluation on the NICO robotic platform demonstrates significant improvements: target-pointing error is reduced by 37%, and action accuracy increases by 29%. These results indicate that the proposed approach effectively overcomes the traditional trade-off between customization capability and robustness inherent in conventional trajectory planning methods.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Generating precise robotic arm trajectories using neural networks
• Customizing trajectory shape for human-robot interaction scenarios
• Ensuring movement accuracy through neural network-based kinematics computation

Innovation

• Neural network generates precise robotic arm trajectories
• Integration of forward kinematics and joint angle generator
• Customizable trajectory shapes for diverse settings