Robotics is a big data problem. To make sense of the physical world, perform tasks well, and generalize across environments, robots need to represent and understand the world at the “correct” level of abstraction. What “correct” should mean remains to be seen. In this talk, I will describe two lines of work that attempt to answer this question from very different perspectives. I will first discuss work on grounding natural language instructions to robot behavior via intermediate representations such as linear temporal logic (LTL). We demonstrate that having the right representations can enable human-robot communication. This is an important problem for robotics, since we envision users using natural language to instruct robots to perform a wide variety of tasks. In the second half, I will discuss recent preliminary work on the theoretical foundations of state abstraction in reinforcement learning, a common framework used in robot learning problems. In particular, we view state abstraction as data compression, and apply results in information theory (rate-distortion theory) to the reinforcement learning setting. Time permitting, I will describe some extensions to the above work, as well as other abstraction-related problems, that I envision my group will pursue at Northeastern.Title: Abstraction in robotics Abstract: Robotics is a big data problem. To make sense of the physical world, perform tasks well, and generalize across environments, robots need to represent and understand the world at the “correct” level of abstraction. What “correct” should mean remains to be seen. In this talk, I will describe two lines of work that attempt to answer this question from very different perspectives. I will first discuss work on grounding natural language instructions to robot behavior via intermediate representations such as linear temporal logic (LTL). We demonstrate that having the right representations can enable human-robot communication. This is an important problem for robotics, since we envision users using natural language to instruct robots to perform a wide variety of tasks. In the second half, I will discuss recent preliminary work on the theoretical foundations of state abstraction in reinforcement learning, a common framework used in robot learning problems. In particular, we view state abstraction as data compression, and apply results in information theory (rate-distortion theory) to the reinforcement learning setting. Time permitting, I will describe some extensions to the above work, as well as other abstraction-related problems, that I envision my group will pursue at Northeastern.