Natural language instructions issued to robots convey intent with multiple layers of spatial abstraction. These layers include high-level goals (“fly to the end of the street”), low-level specifications (“go ten meters south then ten meters east”), or a hybrid (“fly to the lamppost on Hex Street”). In addition, language can express constraints on reaching a goal, such as “go to the lamppost while avoiding the fire hydrant.” Current robot autonomy systems struggle to understand instructions above the lowest layer of spatial abstraction.

Our recent paper published at RSS 2019, titled Planning with State Abstractions for Non-Markovian Task Specifications, approaches this problem with Abstract Product Markov Decision Process (AP-MDP), a novel framework which enables the robot to understand higher-level spatial abstractions in natural language. This framework leverages a deep-learned model to translate natural language into Linear Temporal Logic (LTL), a symbolic form the robot can understand. From LTL, we create motion plans for the robot aligning with goals and constraints spanning different layers of abstraction.

While AP-MDPs were initially tested with a small indoor robot, we realized this framework generalizes to more complex robots and environments. We connected AP-MDPs to a Skydio R1 drone and gave natural language instructions to fly the drone around Brown’s campus! The success of this flight has led to exciting new verticals of research for the lab.

Here’s the video: