Diverse Controllable Diffusion Policy with Signal Temporal Logic (2503.02924v1)

Abstract: 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.

Summary of "Diverse Controllable Diffusion Policy with Signal Temporal Logic"

Yue Meng and Chuchu Fan, in their paper "Diverse Controllable Diffusion Policy with Signal Temporal Logic," address the challenge of generating rule-compliant, diverse behaviors for autonomous systems, focusing specifically on autonomous driving scenarios. They propose a novel approach leveraging Signal Temporal Logic (STL) and Diffusion Models to create a diverse and controllable policy that adheres to predefined rules.

Core Contributions and Methodology

The authors identify a key limitation in current driving simulators: their inability to generate diverse, rule-compliant behaviors due to reliance on either rule-based models or imitation learning from single-outcome datasets. Rule-based models require intricate tuning and often lack diversity, while imitation learning can lead to rule violations. This paper introduces a method that proposes the integration of STL's formal rule specification with diffusion models to overcome these challenges.

1. STL for Behavior Modeling: The research utilizes STL to encode complex traffic rules, which provides a flexible framework for rule specification in autonomous driving scenarios. STL offers robustness in modeling rules, enabling the calibration of driving behaviors based on real-world data.
2. Dataset Augmentation: The authors propose a systematic augmentation of the dataset, generating diverse behaviors through trajectory optimization conditioned on parameterized STL. This addresses the scarcity of diverse, multi-outcome data that hampers learning methods focusing solely on single-outcome datasets.
3. Diffusion Models for Policy Learning: Utilizing Denoising Diffusion Probabilistic Models (DDPM), the paper introduces a diffusion-based learning approach that captures a diverse set of trajectories from the augmented data. An additional RefineNet module enhances the trajectories, ensuring STL compliance and increasing diversity.
4. Empirical Evaluation: The methodology is evaluated on the NuScenes dataset, demonstrating its ability to produce the most diverse rule-compliant trajectories with superior computational efficiency. The results highlight that the proposed approach significantly outperforms baseline methods in generating diverse policy distributions, highlighting improvements in diversity metrics and rule satisfaction rates.

Implications and Future Prospects

The implications of Meng and Fan’s work are significant for the field of autonomous vehicle navigation and related applications. By effectively balancing rule compliance and trajectory diversity, the approach allows for more realistic behavior modeling within simulators, thus bridging the sim-to-real gap in autonomous systems. The ability to control and generate diverse driving characteristics based on STL parameters holds particular promise for realistic agent modeling in virtual environments, potentially enhancing the design of training and testing protocols for autonomous vehicles.

In terms of theoretical advancements, this work underscores the utility of STL in the synthesis and learning of policies under complex, rule-intensive scenarios, suggesting possible extensions to other domains of intelligent autonomous control. Future developments might focus on further scaling these techniques to broader contexts and integrating real-time adaptations for dynamic environments.

Additionally, because the STL-guided diffusion model approach can produce various simulated driver characteristics, it could lead to advancements in human-robot interactions, especially in developing driving agents that can dynamically adapt to human behaviors and improve safety and interaction efficiency within mixed traffic environments.

In summary, Meng and Fan provide a substantial contribution to the fields of robotics and autonomous systems, offering a methodologically sound and practically effective framework for enhancing behavioral diversity and compliance in autonomous driving simulations.