# Generative Robot Policy

RoboFFT, from the PKU-Lingchu joint lab and accepted at CoRL2026, turns the forward noising loss already used to pretrain diffusion and flow policies into an approximate probability ratio, so generative robot policies can be post-trained PPO-style from task success or failure feedback. It was tested on Robomimic simulation tasks, the flow action head of vision-language-action model π₀, and four real-robot manipulation tasks; on eggplant grasping at the same data budget, behavior cloning on pooled data reached 14/20, offline post-training 18/20 and online post-training 20/20.

RoboFFT, from the PKU-Lingchu joint lab and accepted at CoRL2026, turns the forward noising loss already used to pretrain diffusion and flow policies into an approximate probability ratio, so generative robot policies can be post-trained PPO-style from task success or failure feedback. It was tested on Robomimic simulation tasks, the flow action head of vision-language-action model π₀, and four real-robot manipulation tasks; on eggplant grasping at the same data budget, behavior cloning on pooled data reached 14/20, offline post-training 18/20 and online post-training 20/20.

A team from Lingchu Intelligence with Beihang University, Peking University and the Chinese University of Hong Kong introduces FAFM, a frequency-aware flow-matching method that represents robot actions as frequency coefficients tied to real physical time instead of fixed-length discrete sequences, and explicitly constrains the first time derivative while generating actions by flow matching. Demonstrations recorded at 5 Hz, 10 Hz and 20 Hz can then be mapped onto one physical time scale and trained together without downsampling or interpolation. On a Franka arm, integrating FAFM into π₀.₅ raised success from 80% to 95% on grasp-and-place and from 80% to 90% on multi-obstacle avoidance.

A team from Lingchu Intelligence with Beihang University, Peking University and the Chinese University of Hong Kong introduces FAFM, a frequency-aware flow-matching method that represents robot actions as frequency coefficients tied to real physical time instead of fixed-length discrete sequences, and explicitly constrains the first time derivative while generating actions by flow matching. Demonstrations recorded at 5 Hz, 10 Hz and 20 Hz can then be mapped onto one physical time scale and trained together without downsampling or interpolation. On a Franka arm, integrating FAFM into π₀.₅ raised success from 80% to 95% on grasp-and-place and from 80% to 90% on multi-obstacle avoidance.