A Robust Control Framework for Human Motion Prediction

Andrea Bajcsy, Somil Bansal, Ellis Ratner, Claire J. Tomlin, Anca D. Dragan

Abstract: Designing human motion predictors which preserve safety while maintaining robot efficiency is an increasingly important challenge for robots operating in close physical proximity to people. One approach is to use robust control predictors that safeguard against every possible future human state, leading to safe but often too conservative robot plans. Alternatively, intent-driven predictors explicitly model how humans make decisions given their intent, leading to efficient robot plans. However, when the intent model is misspecified, the robot might confidently plan unsafe maneuvers. In this letter, we combine ideas from robust control and intent-driven human modelling to formulate a novel human motion predictor which provides robustness against misspecified human models, but reduces the conservatism of traditional worst-case predictors. Our approach predicts the human states by trusting the intent-driven model to decide only which human actions are completely unlikely. We then safeguard against all likely enough actions, much like a robust control predictor. We demonstrate in simulation and hardware how our approach safeguards against misspecified human intent models while not leading to overly conservative robot plans.