AGILE: A Comprehensive Workflow for Humanoid Loco-Manipulation Learning
2026-03-20 • Robotics
Robotics
AI summaryⓘ
The authors created AGILE, a complete system to help teach robots humanoid skills using reinforcement learning more reliably. AGILE connects all parts of the process, from checking environments to training, testing, and deploying robot controls. They included tools to test motions thoroughly and improve training stability to reduce errors when moving from simulation to real robots. The authors tested AGILE on different robots and tasks, showing it helps robot learning work better in real life. This standardized approach makes developing robot movements more dependable and easier to repeat.
reinforcement learningsim-to-real transferhumanoid robotsrobot training pipelineenvironment verificationpolicy evaluationmotion imitationloco-manipulationUnitree G1Booster T1
Authors
Huihua Zhao, Rafael Cathomen, Lionel Gulich, Wei Liu, Efe Arda Ongan, Michael Lin, Shalin Jain, Soha Pouya, Yan Chang
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 standardizes the policy-development lifecycle to mitigate common sim-to-real failure modes. AGILE comprises four stages: (1) interactive environment verification, (2) reproducible training, (3) unified evaluation, and (4) descriptor-driven deployment via robot/task configuration descriptors. For evaluation stage, AGILE supports both scenario-based tests and randomized rollouts under a shared suite of motion-quality diagnostics, enabling automated regression testing and principled robustness assessment. AGILE also incorporates a set of training stabilizations and algorithmic enhancements in training stage to improve optimization stability and sim-to-real transfer. With this pipeline in place, we validate AGILE across five representative humanoid skills spanning locomotion, recovery, motion imitation, and loco-manipulation on two hardware platforms (Unitree G1 and Booster T1), achieving consistent sim-to-real transfer. Overall, AGILE shows that a standardized, end-to-end workflow can substantially improve the reliability and reproducibility of humanoid RL development.