Probabilistically Safe Robot Planning with Confidence-Based Human Predictions
This addresses safety challenges for robots operating in human environments, representing an incremental advance by integrating confidence-based predictions with existing safe planning frameworks.
The paper tackles the problem of robot safety around humans by developing a method that adjusts human motion predictions based on real-time model confidence, enabling assured autonomous motion with a probabilistic safety certificate. It demonstrates the approach with a quadcopter navigating a human, showing improved safety through quantitative guarantees.
In order to safely operate around humans, robots can employ predictive models of human motion. Unfortunately, these models cannot capture the full complexity of human behavior and necessarily introduce simplifying assumptions. As a result, predictions may degrade whenever the observed human behavior departs from the assumed structure, which can have negative implications for safety. In this paper, we observe that how "rational" human actions appear under a particular model can be viewed as an indicator of that model's ability to describe the human's current motion. By reasoning about this model confidence in a real-time Bayesian framework, we show that the robot can very quickly modulate its predictions to become more uncertain when the model performs poorly. Building on recent work in provably-safe trajectory planning, we leverage these confidence-aware human motion predictions to generate assured autonomous robot motion. Our new analysis combines worst-case tracking error guarantees for the physical robot with probabilistic time-varying human predictions, yielding a quantitative, probabilistic safety certificate. We demonstrate our approach with a quadcopter navigating around a human.