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AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based Policies
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AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based Policies
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Diffusion-based imitation learning improves Behavioral Cloning (BC) on multi-modal decision-making, but comes at the cost of significantly slower inference due to the recursion in the diffusion process. It urges us to design efficient policy generators while keeping the ability to generate diverse actions. To address this challenge, we propose AdaFlow, an imitation learning framework based on flow-based generative modeling. AdaFlow represents the policy with state-conditioned ordinary differential equations (ODEs), which are known as probability flows. We reveal an intriguing connection between the conditional variance of their training loss and the discretization error of the ODEs. With this insight, we propose a variance-adaptive ODE solver that can adjust its step size in the inference stage, making AdaFlow an adaptive decision-maker, offering rapid inference without sacrificing diversity. Interestingly, it automatically reduces to a one-step generator when the action distribution is uni-modal. Our comprehensive empirical evaluation shows that AdaFlow achieves high performance with fast inference speed.
Forward citations
Cited by 6 Pith papers
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HumanoidMimicGen automatically generates large loco-manipulation datasets from few source demonstrations using whole-body planning, enabling visuomotor policies that outperform real-data-only training by 20% on a new ...
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SeFA-Policy: Fast and Accurate Visuomotor Policy Learning with Selective Flow Alignment
Selective Flow Alignment replaces reflow-generated actions with nearby expert actions during training, yielding a one-step flow policy that beats diffusion baselines on 66 simulated and 7 real tasks.
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