Local Path Optimization in Latent Space for Robotic Manipulation
Published:Dec 30, 2025 14:56
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of constrained motion planning in robotics, a common and difficult problem. It leverages data-driven methods, specifically latent motion planning, to improve planning speed and success rate. The core contribution is a novel approach to local path optimization within the latent space, using a learned distance gradient to avoid collisions. This is significant because it aims to reduce the need for time-consuming path validity checks and replanning, a common bottleneck in existing methods. The paper's focus on improving planning speed is a key area of research in robotics.
Key Takeaways
- •Addresses the problem of constrained motion planning in robotics.
- •Proposes a novel local path optimization method in latent space.
- •Uses a learned distance gradient to avoid collisions.
- •Aims to reduce the need for path validity checks and replanning.
- •Demonstrates faster planning speed compared to state-of-the-art algorithms.
Reference
“The paper proposes a method that trains a neural network to predict the minimum distance between the robot and obstacles using latent vectors as inputs. The learned distance gradient is then used to calculate the direction of movement in the latent space to move the robot away from obstacles.”