Robust motion planning and safety benchmarking in human workspaces

It is becoming increasingly feasible for robots to share a workspace with humans. However, for them to do so robustly, they need to be able to smoothly handle the dynamism and uncertainty caused by human motions, and efficiently adapt to newly observed event. While Markov Decision Processes (MDPs) serve as a common model for formulating cost-based approaches for robot planning, other agents are often modeled as part of the environment for the purpose of collision avoidance. This practice, however, has been shown to generate plans that are too inconsistent for humans to confidently interact with. In this work, we show how modeling other agents as part of the environment makes the problem ill-posed, and propose to instead model robot planning in human workspaces as a Stochastic Game. We thus propose a planner with safety guarantees while avoiding overly conservative behavior. Finally, we benchmark the evaluation process in the face of pedestrian modeling error, which has been identified as a major concern in state-of-the-art approaches for robot planning in human workspaces. We evaluate our approach with diverse pedestrian models based on real-world observations, and show that our approach is collision-safe when encountering various pedestrian behaviors, even when given inaccurate predictive models.