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Long past are the days where designing for maneuver means principally designing for formations in open swathes of desert or the wide passes between hills in European plains. The army of the future, like the army of the present, will fight in cities, and they will want machines that can operate in all the three dimensionalities of that space without needing step-by-step human supervision.<\/p>\n\n\n\n
\u201cIn some ways teleoperation is also a research problem. We know how to teleoperate vehicles in a fairly benign environment and even that is very labor consuming,\u201d Kott said.<\/p>\n\n\n\n
\u201cIf you go into more complicated environments, for example, tunnels and holes in the urban rubble, teleoperation becomes exceedingly difficult. The amount of information that the soldier who operates that vehicle needs to actually do teleoperation at any reasonable speed is huge.\u201d<\/p>\n\n\n\n
Far from the ease of massive, clean, online datasets, or even the relatively clean data of cars collecting information on city streets, the Army will have to collect data it needs to teach robots to maneuver each of these obstacles: tunnels, manholes, urban rubble, hills and forests, craters and anything else someone might reasonably expect on a battlefield.<\/p>\n\n\n\n
\u201cThe problem of retraining and relearning changing environments continues to be a very difficult problem which doesn\u2019t really have a particularly good solution. So, in fact, no reliable solutions,\u201d Kott said.<\/p>\n\n\n\n
\u201cA good deal of it remains a matter of research on how to build machine intelligence for this kind of environment. I will say it is unreasonable to say that we have answers to these questions.\u201d<\/p>\n\n\n\n
Autonomy here is best thought of as meeting two distinct goals: saving the labor of a human doing the task either while physically present or remotely, and enabling vehicles to work in denied or difficult environments with low bandwidth. Sending the data back that is needed for a human to pilot, say, a remote-control ground vehicle is tricky in permissive environments, virtually impossible in dense cities and especially unlikely if the robot is operating underground. Collecting useful information for a remote operator means adding more sensors than the vehicle needs, which again increases the needed bandwidth and makes communication difficult.<\/p>\n\n\n\n
If, instead, the robots can operate largely on their own \u2014 whether it is a quadcopter autonomously flying around a city block or a futuristic crawling machine maneuvering through sewers in order to deliver supplies to troops pinned down in a difficult-to-reach place \u2014 then the machines will need to do far less to keep humans informed. Maybe it\u2019s a low-bandwidth broadcast of a GPS location, or maybe just a one-sentence generated update relayed to a tablet indicating progress. Developing to both the limits and strengths of autonomous machines means devising new ways for the humans to collaborate with the robots working alongside them.<\/p>\n\n\n\n
Team all that you can team<\/b><\/p>\n\n\n\n
To make that collaboration work, ARL is studying \u201chuman agent teaming,\u201d which borrows lessons from human-machine interface work but is much more about the relationships between entities than interpretation of one by the other.<\/p>\n\n\n\n
\u201cIt\u2019s not just a machine and because it\u2019s a capable of some actions and decisions on its own, it becomes much more difficult for the human to deal with it,\u201d Kott said.<\/p>\n\n\n\n
\u201cTeaming means that the agent needs to understand and that human needs to understand the agent.\u201d<\/p>\n\n\n\n
To that end, the key thus far has been transparency. If a human knows that the machine is communicating with it, even if it doesn\u2019t understand perfectly what the machine is doing, that the machine is communicating is, itself, a way to build trust between the human and the robot. This goes both ways: for the machines to work with humans, it helps if they can understand and anticipate how the humans are going to act.<\/p>\n\n\n\n
\u201cWe are also looking at how machines can learn about what humans do. We\u2019re doing research on [electroencephalography] signals. By analyzing EEG signals, machines are getting better and better at figuring out what the human is doing \u2014 is looking at, for example \u2014 or what kind of several possible tasks that human is about to undertake,\u201d Kott said.<\/p>\n\n\n\n
\u201cWe don\u2019t expect that we\u2019ll put the EEG helmet on the soldier in the battlefield, but it\u2019s a step in the direction of artificial agents being able to understand something about humans and anticipate what humans might want to do.\u201d<\/p>\n\n\n\n
Consider the earlier example of the resupply robot crawling through tunnels. For both the humans waiting on the supplies and the humans who sent it, the way the robot gets into place is less important than it getting there at all. If that robot is able to transmit directions it\u2019s traveling and just simple info about what it is doing to get where it needs to be, the humans waiting for it can operate with some degree of trust, even if they\u2019re skeptical of the route.<\/p>\n\n\n\n
\u201cAll this is very helpful to the soldier to feel more confident that this agent is not a rogue agent. It\u2019s not off doing something weird and entirely unpredictable,\u201d Kott said.<\/p>\n\n\n\n
\u201cThat turns out to be very helpful.\u201d<\/p>\n\n\n\n
Getting to that future means studying what we can now, with soldiers practicing teaming in virtual environments, and it means engineering those legged robots to overcome limitations on power, size and noise to get to a good-enough design. Autonomy is a science of iteration until it becomes a science of observation. The machines that will crawl and fly and drive supplies and scout future missions are likely to find themselves in far less permissive environments than the present, and the way through a contested electromagnetic spectrum is autonomy.<\/p>\n\n\n\n
Even if it means a few false starts in the desert to get there.<\/p>\n","protected":false},"excerpt":{"rendered":"
Clean data is hard to find on the battlefield. So how can the Army\u2019s robotics develop without it?<\/p>\n","protected":false},"author":7,"featured_media":47764,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_yoast_wpseo_meta-robots-noindex":"","_yoast_wpseo_meta-robots-nofollow":"","_yoast_wpseo_canonical":"","_acf":"","_yoast_wpseo_primary_category":21,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","_smg_distribution_targets":[]},"categories":[106,29,32,21,111],"tags":[],"coauthors":[3021],"class_list":["post-15876","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-daily-brief","category-home","category-newsletters","category-unmanned","category-unmanned-systems"],"acf":{"subheadline":"","legacy_arc_id":"WMWA3WUBRBFHPLUDSZUE67E7VM","arc_canonical_url":"\/unmanned\/2018\/12\/13\/the-dirty-secret-behind-autonomous-machines\/","remove_feature_photo":false,"is_sponsored":false,"subtype":"","redirect_url":"","disable_inline_ads":false,"native_logo_pretext":"Presented By:"},"yoast_head":"\n