Diverse Controllable Diffusion Policy with Signal Temporal Logic
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.
67c9d3801e7c4f57b44a958d
Yue Meng
claimed_verified
2025-03-06T19:50:39.656Z
668e100b97171f3399e07f5d
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/pgzatAZut-uoXOBOwgiFB.jpeg
Yue Meng
user
yuemithucsd
67c9d3801e7c4f57b44a958e
Chuchu fan
Diverse Controllable Diffusion Policy with Signal Temporal Logic
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.
Combined Signal Temporal Logic and diffusion models generate diverse, rule-compliant behaviors for autonomous systems in simulations with high efficiency and performance.
Signal Temporal Logic STL Diffusion Models diffusion policy trajectory optimization augmentation dataset real-world datasets closed-loop testing rule satisfaction