Search Results for author: Shu Ishida

Found 3 papers, 2 papers with code

Towards real-world navigation with deep differentiable planners

1 code implementation CVPR 2022 Shu Ishida, João F. Henriques

To avoid the potentially hazardous trial-and-error of reinforcement learning, we focus on differentiable planners such as Value Iteration Networks (VIN), which are trained offline from safe expert demonstrations.

Imitation Learning Motion Planning +3

LangProp: A code optimization framework using Language Models applied to driving

1 code implementation18 Jan 2024 Shu Ishida, Gianluca Corrado, George Fedoseev, Hudson Yeo, Lloyd Russell, Jamie Shotton, João F. Henriques, Anthony Hu

LangProp is a framework for iteratively optimizing code generated by large language models (LLMs) in a supervised/reinforcement learning setting.

Autonomous Driving Code Generation +2

Cannot find the paper you are looking for? You can Submit a new open access paper.