Highlights:
- Introduces a framework for safe planning in interactive environments such as self-driving vehicles among human actors.
- Combines iterative policy updates with adversarially robust conformal prediction (CP) to maintain safety guarantees.
- Addresses violations of data exchangeability in conformal prediction for interactive settings.
- Applies policy-to-trajectory sensitivity analysis to account for distribution shifts.
TLDR:
Researchers propose a novel framework that ensures safe decision-making for autonomous agents even in dynamic, interactive environments by using adversarially robust conformal prediction and iterative policy updates.
A team of researchers, including Omid Mirzaeedodangeh, Eliot Shekhtman, Nikolai Matni, and Lars Lindemann, has unveiled a pioneering method for safer autonomous planning titled *’Safe Planning in Interactive Environments via Iterative Policy Updates and Adversarially Robust Conformal Prediction’*. This work, published on arXiv under the Systems and Control category, addresses one of the most complex challenges in artificial intelligence and robotics — ensuring safety guarantees in environments where human and machine behaviors interact dynamically.
Autonomous systems, such as self-driving cars and aerial robots, often operate in environments where human behavior cannot be easily predicted. Traditional models that guarantee safety rely on static data assumptions, but in interactive environments, an autonomous agent’s actions can influence how humans or other agents respond. This feedback loop leads to interaction-driven distribution shifts that break the foundational assumptions used in conformal prediction (CP) methods. These shifts make it difficult to maintain valid safety assurances over time, potentially putting both the system and humans at risk.
To overcome this limitation, the authors present an iterative policy update framework that uses adversarially robust conformal prediction. In this setup, the system performs standard CP in each episode using observed environmental data, then analytically adjusts the resulting safety guarantees when updating its control policy. This adjustment accounts for changes in the environment caused by the policy updates themselves — effectively mitigating the circular dependency that invalidates traditional CP. By incorporating policy-to-trajectory sensitivity analysis, the model quantifies how new policies influence observed states and trajectories, leading to an episodic open-loop planning framework with provable safety guarantees.
Beyond theoretical innovation, Mirzaeedodangeh and colleagues provide contraction-based proofs ensuring that both the iterative CP adjustments and the policy updates converge to a stable solution. Empirical validation in a 2D car-pedestrian simulation confirms that the proposed approach can maintain rigorous safety margins while the autonomous agent adapts to its surroundings. These results represent the first formal proof of valid safety guarantees in such interactive, reactive settings—paving the way for safer autonomous navigation in complex, real-world environments.
Source:
Source:
Original research paper: Mirzaeedodangeh, O., Shekhtman, E., Matni, N., & Lindemann, L. (2025). *Safe Planning in Interactive Environments via Iterative Policy Updates and Adversarially Robust Conformal Prediction.* arXiv:2511.10586v1 [eess.SY]. https://doi.org/10.48550/arXiv.2511.10586