Conformal prediction enables safe robot navigation without uncertainty assumptions
New framework guarantees risk thresholds even with unknown data distributions
Safe navigation in dynamic environments typically requires accurate knowledge of uncertainty distributions—a challenging requirement given limited data or imperfect information. To overcome this, researchers Junsik Eom and Tulga Ersal introduce a distribution-free risk-aware model predictive control (RA-MPC) framework. It leverages conformal spectral risk control, an extension of conformal risk control, to produce prediction sets that guarantee spectral risk constraints are satisfied regardless of the true uncertainty distribution. The framework allows users to specify a risk threshold, and the control system statistically ensures compliance even under uncertainty misspecification.
In simulated vehicle obstacle avoidance tests, the proposed framework outperformed baseline RA-MPC by improving safety margins and reducing computational solve time. This advance is particularly relevant for autonomous systems operating in real-world settings where uncertainty is complex and poorly characterized. By removing the need for distributional assumptions, the method enables more reliable decision-making in robotics, autonomous driving, and other safety-critical applications. The work has been submitted to IEEE Robotics and Automation Letters.
- Framework uses conformal spectral risk control to generate prediction sets without distribution assumptions
- Empirically validated in vehicle obstacle avoidance, showing improved safety and reduced solve time
- Guarantees spectral risk constraint satisfaction even when uncertainty model is misspecified
Why It Matters
Enables safer autonomous navigation in real-world environments where uncertainty is unpredictable and data is limited.