Generative Latent Motion Planning and Reinforcement Learning for Legged Locomotion
Name
miller-ajm4-phd-eecs-2025-thesis.pdf
Description
Thesis PDF
Size
56.84 MB
Format
Adobe PDF
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361f83338b42d11c4fd9c1caf224be98
Author(s)
Miller, Adam Joseph
Advisor(s)
Kim, Sangbae
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
In recent years, reinforcement learning has demonstrated its promise as a powerful tool for developing innovative and advanced control systems for legged robots. The method’s robustness, versatility, and generality have made it a prime candidate for future robotic systems deployed in the real world. Through the development of more advanced machine learning algorithms and more reliable and efficient physics simulators, reinforcement learning continues to improve and enable new, dynamic, and agile capabilities. While the results are often impressive and the tools relatively beginner-friendly, there remain impediments to scalable and reliable progress. Poor reward function scaling, challenges balancing exploration versus exploitation, and misalignment from the engineer’s intent are roadblocks to better performance. To get beyond these limitations, new tools and frameworks are necessary. In this work, I present novel methods to address these challenges and extend the capabilities of reinforcement learning on robot hardware. Through the quantification of the distributional sim-to-real gap, simulation model optimization for hardware matching, latent space motion sequence planning, and latent style training, I demonstrate never-before-seen performance on legged hardware.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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