title: Diverse Controllable Diffusion Policy with Signal Temporal Logic
summary: Generating realistic simulations is critical for autonomous system
  applications such as self-driving and human-robot interactions. However,
  driving simulators nowadays still have difficulty in generating controllable,
  diverse, and rule-compliant behaviors for road participants: Rule-based models
  cannot produce diverse behaviors and require careful tuning, whereas
  learning-based methods imitate the policy from data but are not designed to
  follow the rules explicitly. Besides, the real-world datasets are by nature
  "single-outcome", making the learning method hard to generate diverse
  behaviors. In this paper, we leverage Signal Temporal Logic (STL) and Diffusion
  Models to learn controllable, diverse, and rule-aware policy. We first
  calibrate the STL on the real-world data, then generate diverse synthetic data
  using trajectory optimization, and finally learn the rectified diffusion policy
  on the augmented dataset. We test on the NuScenes dataset and our approach can
  achieve the most diverse rule-compliant trajectories compared to other
  baselines, with a runtime 1/17X to the second-best approach. In the closed-loop
  testing, our approach reaches the highest diversity, rule satisfaction rate,
  and the least collision rate. Our method can generate varied characteristics
  conditional on different STL parameters in testing. A case study on human-robot
  encounter scenarios shows our approach can generate diverse and
  closed-to-oracle trajectories. The annotation tool, augmented dataset, and code
  are available at https://github.com/mengyuest/pSTL-diffusion-policy.
paper_authors._id: 67c9d3801e7c4f57b44a958d
paper_authors.name: Yue Meng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-06T19:50:39.656Z
paper_authors._id: 668e100b97171f3399e07f5d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/pgzatAZut-uoXOBOwgiFB.jpeg
paper_authors.fullname: Yue Meng
paper_authors.type: user
paper_authors.user: yuemithucsd
paper_authors._id: 67c9d3801e7c4f57b44a958e
paper_authors.name: Chuchu fan
paper_title: Diverse Controllable Diffusion Policy with Signal Temporal Logic
paper_summary: Generating realistic simulations is critical for autonomous system
  applications such as self-driving and human-robot interactions. However,
  driving simulators nowadays still have difficulty in generating controllable,
  diverse, and rule-compliant behaviors for road participants: Rule-based models
  cannot produce diverse behaviors and require careful tuning, whereas
  learning-based methods imitate the policy from data but are not designed to
  follow the rules explicitly. Besides, the real-world datasets are by nature
  "single-outcome", making the learning method hard to generate diverse
  behaviors. In this paper, we leverage Signal Temporal Logic (STL) and Diffusion
  Models to learn controllable, diverse, and rule-aware policy. We first
  calibrate the STL on the real-world data, then generate diverse synthetic data
  using trajectory optimization, and finally learn the rectified diffusion policy
  on the augmented dataset. We test on the NuScenes dataset and our approach can
  achieve the most diverse rule-compliant trajectories compared to other
  baselines, with a runtime 1/17X to the second-best approach. In the closed-loop
  testing, our approach reaches the highest diversity, rule satisfaction rate,
  and the least collision rate. Our method can generate varied characteristics
  conditional on different STL parameters in testing. A case study on human-robot
  encounter scenarios shows our approach can generate diverse and
  closed-to-oracle trajectories. The annotation tool, augmented dataset, and code
  are available at https://github.com/mengyuest/pSTL-diffusion-policy.
paper_ai_summary: Combined Signal Temporal Logic and diffusion models generate diverse, rule-compliant behaviors for autonomous systems in simulations with high efficiency and performance.
paper_ai_keywords: Signal Temporal Logic STL Diffusion Models diffusion policy trajectory optimization augmentation dataset real-world datasets closed-loop testing rule satisfaction