A Bayesian Approach to Safe Imitation Learning For AIs and Robots

In his wonderful 1943 short story Q.U.R., science fiction writer Anthony Boucher writes about robots that are able to learn by viewing hundreds of images:

"I got one of those new electronic cameras - you know, one thousand exposures per second... So we took pictures of Guzub making a Three Planets, and I could construct this one to do it exactly right down to the thousandth of a second. The proper proportion of vuzd, in case you're interested, works out to three-point-six-five-four-seven eight-two-three drops. It's done with a flip of the third joint of the tentacle on the down beat. It didn't seem right to use Guzub to make a robot that would compete with him and probably drive him out of business, so we've promised him a generous pension from the royalties on usuform barkeeps."
(Read more about Anthony Boucher's usuform robot bartender)

Now, doughty researchers are working to improve imitation learning:

DropoutDAgger: A Bayesian Approach to Safe Imitation Learning

While imitation learning is becoming common practice in robotics, this approach often suffers from data mismatch and compounding errors. DAgger is an iterative algorithm that addresses these issues by continually aggregating training data from both the expert and novice policies, but does not consider the impact of safety. We present a probabilistic extension to DAgger, which uses the distribution over actions provided by the novice policy, for a given observation. Our method, which we call DropoutDAgger, uses dropout to train the novice as a Bayesian neural network that provides insight to its confidence. Using the distribution over the novice's actions, we estimate a probabilistic measure of safety with respect to the expert action, tuned to balance exploration and exploitation. The utility of this approach is evaluated on the MuJoCo HalfCheetah and in a simple driving experiment, demonstrating improved performance and safety compared to other DAgger variants and classic imitation learning.

Modern-day roboticists might be interested in Boucher's distinction between manlike robots and usuform robots.

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