Counterfactual Reasoning about Intent for Interactive Navigation in Dynamic Environments

Many modern robotics applications require robots to function autonomously in dynamic environments including other decision making agents, such as people or other robots. This calls for fast and scalable interactive motion planning that is intention-aware. We present a real-time motion planning framework that brings together a few key components including an interactive motion model for other agents and counterfactual reasoning over possible movement intentions of other agents. This yields a light-weight iterative planner that enables fluid motion when avoiding pedestrians, in parallel with goal inference for longer range movement prediction. This motion planning framework is coupled with a novel distributed visual tracking method that provides reliable and robust models for the current belief-state of the monitored environment. This combined approach represents a computationally efficient alternative to previously studied policy learning methods that often require significant offline training or calibration and do not yet scale to densely populated environments. We validate this framework with experiments involving multi-robot and human-robot navigation. Also, we further validate the tracker component on unconstrained pedestrian data sets.