AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based Policies

Understand

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.