This work deals with the problem of egocentric humanoid navigation. Given egocentric video, how can we predict a navigation trajectory for a humanoid robot or assistive humanoid policies to help people navigate? Boxiao basically asked this question: how close are humanoid robots now from actually being deployed in the real world? The surprise is that the answer is not really. We're still quite far from it. This paper wants to take one step towards making that a reality. Boxiao and team approached this problem from several fronts. First, they make the problem statement closer to that reality. Most of the prior works study an environment where obstacles are mostly static, or there is no more than one or two person moving in front of the robot and that's it. Which is very far from the reality! The authors decided to study in the real world dynamic environments. They collected data by just walking in very busy streets and specifically go out to find streets and times where a lot of people and cars are found. These are the two primary dynamic obstacles in this study. The policy needs to find a trajectory that can avoid both the static and the dynamics obstacles, just from egocentric video, which makes the problem studied very close to a real world policy. And this is on the input side. On the output side, we want the policy to be to learn what we call human-like active information guiding behavior, which corresponds to what humans would normally do, like rotate our heads and look for useful information. For example, before we cross the road, we would first look to the sides before we actually cross. “We want our robot,” Boxiao explains, “to learn these information or behaviors as well. So we specifically include such behaviors 19 DAILY ICCV Thursday Boxiao Pan
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