ADEPT: Adaptive Diffusion Environment for Policy Transfer Sim-to-Real
Youwei Yu, Junhong Xu, and Lantao Liu

TL;DR
ADEPT introduces an adaptive diffusion-based environment generation method for zero-shot sim-to-real policy transfer, enhancing training diversity and robustness in complex, unstructured environments.
Contribution
It leverages Denoising Diffusion Probabilistic Models to dynamically generate diverse environments tailored to policy performance, advancing environment generation for reinforcement learning.
Findings
ADEPT outperforms procedural and natural environments in navigation tasks.
The method improves policy robustness and transferability.
A new multi-layer map representation accelerates environment benchmarking.
Abstract
Model-free reinforcement learning has emerged as a powerful method for developing robust robot control policies capable of navigating through complex and unstructured environments. The effectiveness of these methods hinges on two essential elements: (1) the use of massively parallel physics simulations to expedite policy training, and (2) an environment generator tasked with crafting sufficiently challenging yet attainable environments to facilitate continuous policy improvement. Existing methods of outdoor environment generation often rely on heuristics constrained by a set of parameters, limiting the diversity and realism. In this work, we introduce ADEPT, a novel \textbf{A}daptive \textbf{D}iffusion \textbf{E}nvironment for \textbf{P}olicy \textbf{T}ransfer in the zero-shot sim-to-real fashion that leverages Denoising Diffusion Probabilistic Models to dynamically expand existing…
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Taxonomy
TopicsReinforcement Learning in Robotics · Robotic Path Planning Algorithms · Autonomous Vehicle Technology and Safety
