System und Verfahren zum Imitationslernen in Robotern für Komplexes Aufgabenlernen

Details

Veröffentlichungs-Nr.

EP4549107

Aktenzeichen

EP24207212

Anmeldetag

17. Oktober 2024

Veröffentlichung

7. Mai 2025

Rechtsraum

EP

IPC

B25J9/16G05B19/423

Abstract

The embodiments of present disclosure herein address unresolved problem of key capabilities that an intelligent robot must possess to perform complex and long-horizon object manipulation tasks without human intervention. The present disclosure overcomes above mentioned challenges by decoupling imitation learning task into two parts using a decouple neural network architecture comprising two modules. A high-level module that segments out sequence of primitive actions from video of human demonstration of a complex task and a low-level module that executes those primitives sequentially on robot. The high-level module, Task Sequencing Network (TSNet), is a hybrid neural network composed of Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) with Connectionist Temporal Classification (CTC) loss, while low-level controller is built using dynamic movement primitives (DMP). The network is trained in a supervised learning setting. The CNN, RNN with CTC combination helps in efficiently segmenting task primitives in sequence for given input video demonstrations.

Anmelder

Firma

Tata Consultancy Services Limited

Land

🇮🇳 Indien

Fachgebiete