Trajectory optimization using neural networks

US12337485B2 · kind B2 · utility

Assignee

• Embodied Intelligence Inc. · US

Inventors

• Haoran Tang · Emeryville, US
• Xi Chen · Annandale, US
• Yan Duan · Berkeley, US
• Nikhil Mishra · Irvine, US
• Shiyao Wu · Emeryville, US
• Maximilian Sieb · Emeryville, US
• Yide Shentu · Berkeley, US

Key dates

• Filing date: Jun 24, 2024
• Grant date: Jun 24, 2025
• Expiry date: Jun 24, 2044

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N5/045
• WIPO field: Handling
• WIPO sector: Mechanical engineering

Abstract

Various embodiments of the technology described herein generally relate to systems and methods for trajectory optimization with machine learning techniques. More specifically, certain embodiments relate to using neural networks to quickly predict optimized robotic arm trajectories in a variety of scenarios. Systems and methods described herein use deep neural networks to quickly predict optimized robotic arm trajectories according to certain constraints. Optimization, in accordance with some embodiments of the present technology, may include optimizing trajectory geometry and dynamics while satisfying a number of constraints, including staying collision-free and minimizing the time it takes to complete the task.