@misc{9e060c64-620b-49ba-b1c8-495e624f875c,
  abstract     = {{The field of general-purpose robotics has recently embraced powerful probabilistic diffusion-based models to learn the complex embodiment behaviours. However, existing models often come with significant trade-offs, namely high computational costs for inference and a fundamental inability to quantify output uncertainty. We introduce Normalizing Flows Policy (NF-P), a conditional normalizing flow-based visuomotor policy for bi-manual manipulation. NF-P learns a conditional density over action sequences and enables single-pass generative sampling with tractable likelihood computation. Using this property, we propose two inference-time optimization strategies: Stochastic Batch Selection, which selects the highest-likelihood trajectory among sampled candidates, and Gradient Refinement, which directly ascends the log-likelihood to improve action quality. In both simulation and real robot experiments, NF-P achieves promising success rates compared to the baseline. In addition to improved task performance, NF-P demonstrates faster training and lower inference latency. These results establish normalizing flows as a competitive and computationally efficient visuomotor policy, particularly for real-time, uncertainty-aware robotic control.}},
  author       = {{Li, Jialong and Kristoffersson Lind, Simon and Xie, Wenrui and Stenmark, Maj and Krueger, Volker}},
  language     = {{eng}},
  note         = {{Preprint}},
  title        = {{Normalizing flows are capable models for bi-manual visuomotor policy}},
  url          = {{https://arxiv.org/abs/2509.21073}},
  year         = {{2026}},
}