T-2: Putting perception into action

Saturday, 27 August, 10:30 - 12:15 Pacific Time (UTC -7)
Location: Grand Ballroom B-C
Tutorial

In this tutorial, we will give a detailed look at optimal control models of continuous psychophysics tasks and our method for inverse optimal control. Specifically, participants will learn step by step how to build an optimal control agent for a target tracking task with perceptual uncertainty, action variability, behavioral costs, and subjective beliefs. This includes a brief introduction to optimal control in the linear-quadratic Gaussian (LQG) framework. We will then give guidance on how to turn an intuitive understanding of a task into concrete choices for stochastic linear dynamical systems and cost functions. To introduce inverse optimal control, we emphasize the distinction between the subject's point of view and the researcher's point of view using probabilistic graphical models. From the subject's point of view, an internal model describes the task dynamics and subjective goals. From the researcher's point of view, a statistical model of the subject's behavior is used to infer the parameters of the subject's internal model. Participants will get an intuitive understanding of the derivations for probabilistic inverse optimal control based on this conceptual framework.

The second half of the tutorial is a hands-on lesson that goes into detail about the implementation of (inverse) optimal control. We showcase our Python library for inverse optimal control in the LQG framework, which is based on the automatic differentiation package jax. In a Colab notebook, participants will learn how to use the library to define and simulate optimal control agents and develop an intuition for how the model parameters influence the trajectories generated by the agent. We will also show how the mathematical derivations for probabilistic inverse optimal control from the first part of the tutorial are implemented and how to perform Bayesian inference using the probabilistic programming package numpyro. Example experiments include a manual tracking task with humans and a gaze tracking task with non-human primates.

Constantin Rothkopf

Constantin Rothkopf

TU Darmstadt

Dominik Straub

Dominik Straub

TU Darmstadt