Chrome Extension
WeChat Mini Program
Use on ChatGLM

PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations.

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(2022)

Cited 7|Views81
No score
Abstract
Evolution Strategy (ES) algorithms have shown promising results in training complex robotic control policies due to their massive parallelism capability, simple implementation, effective parameter-space exploration, and fast training time. However, a key limitation of ES is its scalability to large capacity models, including modern neural network architectures. In this work, we develop Predictive Information Augmented Random Search (PI-ARS) to mitigate this limitation by leveraging recent advancements in representation learning to reduce the parameter search space for ES. Namely, PI-ARS combines a gradient-based representation learning technique, Predictive Information (PI), with a gradient-free ES algorithm, Augmented Random Search (ARS), to train policies that can process complex robot sensory inputs and handle highly nonlinear robot dynamics. We evaluate PI-ARS on a set of challenging visual-locomotion tasks where a quadruped robot needs to walk on uneven stepping stones, quincuncial piles, and moving platforms, as well as to complete an indoor navigation task. Across all tasks, PI-ARS demonstrates significantly better learning efficiency and performance compared to the ARS baseline. We further validate our algorithm by demonstrating that the learned policies can successfully transfer to a real quadruped robot, for example, achieving a 100% success rate on the real-world stepping stone environment, dramatically improving prior results achieving 40% success.
More
Translated text
Key words
ARS baseline,complex robot sensory inputs,complex robotic control policies,effective parameter-space exploration,evolution strategy algorithms,evolution-learned visual-locomotion,gradient-based representation,gradient-free ES algorithm,learned policies,nonlinear robot dynamics,parameter search space,PI-ARS,predictive information augmented random search,predictive information representations,quadruped robot,representation learning,visual-locomotion tasks
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined