A comprehensive end-to-end workflow for humanoid loco-manipulation that standardizes sim-to-real transfer.
March 23, 2026
Original Paper
AGILE: A Comprehensive Workflow for Humanoid Loco-Manipulation Learning
arXiv · 2603.20147
The Takeaway
Provides a systematic infrastructure (verification, training, evaluation, and deployment) for humanoid robots. It moves the field beyond isolated 'cool videos' toward reproducible engineering pipelines for complex embodied AI.
From the abstract
Recent advances in reinforcement learning (RL) have enabled impressive humanoid behaviors in simulation, yet transferring these results to new robots remains challenging. In many real deployments, the primary bottleneck is no longer simulation throughput or algorithm design, but the absence of systematic infrastructure that links environment verification, training, evaluation, and deployment in a coherent loop.To address this gap, we present AGILE, an end-to-end workflow for humanoid RL that sta