aiqbench analysis
This research introduces SteerGenPO, a novel framework that decouples the stochastic exploration of diffusion-based policies from the deterministic control required for stable robotic deployment. By utilizing a learned latent actor to steer generative outputs into precise actions, the method significantly enhances robustness in high-dimensional systems like humanoid locomotion tasks without sacrificing learning efficiency. The approach represents a meaningful advancement in applying advanced machine learning techniques specifically tailored to solve stability challenges inherent in physical robot control.
Steering Generative Reinforcement Learning into Stable Robotic Controller
arXiv:2606.16572v1 Announce Type: new Abstract: Diffusion and flow-based generative policies provide a powerful policy class for reinforcement learning by inducing rich stochastic exploration through iterative action generation. However, the stochasticity of diffusion policies is not suitable for stable and precise control in high-dimensional robotic systems, where small action variations can accumulate into inconsistent motion and reduced robustness. To address this issue, we propose SteerGenPO, a latent-space reinforcement learning framework that steers a trained generative policy into a robust deterministic robotic controller. The key idea is to replace stochastic latent sampling of the trained generative policy with a learned latent actor that predicts a state-dependent latent input for the generative policies. This separates exploration and control: stochastic generative sampling provides diverse action proposals during policy learning, while deterministic latent steering provides stable and adaptive control at deployment. We evaluate SteerGenPO on six Isaac Lab benchmarks and a Unitree G1 locomotion task. The results show SteerGenPO improves over both classical RL and generative RL baselines, while its deterministic latent steering produces more stable inference-time behaviors and more reliable command responses.
Abstract:Diffusion and flow-based generative policies provide a powerful policy class for reinforcement learning by inducing rich stochastic exploration through iterative action generation. However, the stochasticity of diffusion policies is not suitable for stable and precise control in high-dimensional robotic systems, where small action variations can accumulate into inconsistent motion and reduced robustness. To address this issue, we propose SteerGenPO, a latent-space reinforcement learning framework that steers a trained generative policy into a robust deterministic robotic controller. The key idea is to replace stochastic latent sampling of the trained generative policy with a learned latent actor that predicts a state-dependent latent input for the generative policies. This separates exploration and control: stochastic generative sampling provides diverse action proposals during policy learning, while deterministic latent steering provides stable and adaptive control at deployment. We evaluate SteerGenPO on six Isaac Lab benchmarks and a Unitree G1 locomotion task. The results show SteerGenPO improves over both classical RL and generative RL baselines, while its deterministic latent steering produces more stable inference-time behaviors and more reliable command responses.
Robotics (cs.RO)
arXiv:2606.16572 [cs.RO]
(or arXiv:2606.16572v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.16572 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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