Hexapod Robot Gets Even Better at Being Indestructible

Any kind of damage is just a flesh wound for this adaptable walking hexapod

2 min read
Hexapod Robot Gets Even Better at Being Indestructible

Last year, we wrote about a hexapod robot that could teach itself to walk after you chopped its leg off. It was awesome. It still is awesome, because if you have any experience with robots, you know that they malfunction almost continuously. Last week, we saw some updated results from the same researchers, and after perusing both papers with much scratching-of-heads and stabbing-out-of-eyes, we've figured out what's new and exciting for 2014.

First, let's take a look at the aforementioned awesomeness of a robot that can keep on coming for you even after it loses a leg:

Okay, so here's what the newness seems to be: in last year's paper, the robots relied on experimental discovery to determine the best compensatory gait to overcome whatever damage they were experiencing. So, whenever something bad happened, they'd try a whole bunch of stuff to gradually figure out a better gait. This worked fine, but took about 20 minutes each time, which (I guess) seemed like a very long time to the researchers.

In the more recent paper, the damage adaptation time has been reduced to 2 minutes by eliminating most of the experimental discovery step. Instead, the robot runs through a bunch of pre-discovered gaits (a "six dimensional behavioral repertoire"), evaluates a series of them, and then selects the best. This repertoire is quite extensive, containing approximately 13,000 different gaits. It took two weeks to create using a robot model running in simulation (20 million iterations of a gait generation algorithm), but since you only ever have to do it one single time for each robot (and it can be done at design time before deployment), it saves you time in the field, which is where time is actually important.

The difference between 20 minutes of work to develop a new gait and 2 minutes of work is definitely significant for small robots, which are limited in both computational power and battery life. As researchers seek to emphasize simplicity and low cost (in order to deploy more robots efficiently), anything that can make each individual robot more capable, even a little bit, can make it much more likely that a given task will be completed successfully. 

[ Paper ] via [ Medium ] via [ Gizmodo ]

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How the U.S. Army Is Turning Robots Into Team Players

Engineers battle the limits of deep learning for battlefield bots

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Robot with threads near a fallen branch

RoMan, the Army Research Laboratory's robotic manipulator, considers the best way to grasp and move a tree branch at the Adelphi Laboratory Center, in Maryland.

Evan Ackerman

“I should probably not be standing this close," I think to myself, as the robot slowly approaches a large tree branch on the floor in front of me. It's not the size of the branch that makes me nervous—it's that the robot is operating autonomously, and that while I know what it's supposed to do, I'm not entirely sure what it will do. If everything works the way the roboticists at the U.S. Army Research Laboratory (ARL) in Adelphi, Md., expect, the robot will identify the branch, grasp it, and drag it out of the way. These folks know what they're doing, but I've spent enough time around robots that I take a small step backwards anyway.

This article is part of our special report on AI, “The Great AI Reckoning.”

The robot, named RoMan, for Robotic Manipulator, is about the size of a large lawn mower, with a tracked base that helps it handle most kinds of terrain. At the front, it has a squat torso equipped with cameras and depth sensors, as well as a pair of arms that were harvested from a prototype disaster-response robot originally developed at NASA's Jet Propulsion Laboratory for a DARPA robotics competition. RoMan's job today is roadway clearing, a multistep task that ARL wants the robot to complete as autonomously as possible. Instead of instructing the robot to grasp specific objects in specific ways and move them to specific places, the operators tell RoMan to "go clear a path." It's then up to the robot to make all the decisions necessary to achieve that objective.

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