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Humans might not be directly in the loop at all times, but the idea is that humans and robots are more effective when working together as a team.

When the most recent phase of the Robotics Collaborative Technology Alliance program began in , Stump says, "we'd already had many years of being in Iraq and Afghanistan, where robots were often used as tools. We've been trying to figure out what we can do to transition robots from tools to acting more as teammates within the squad.

RoMan gets a little bit of help when a human supervisor points out a region of the branch where grasping might be most effective. The robot doesn't have any fundamental knowledge about what a tree branch actually is, and this lack of world knowledge what we think of as common sense is a fundamental problem with autonomous systems of all kinds.

Having a human leverage our vast experience into a small amount of guidance can make RoMan's job much easier. And indeed, this time RoMan manages to successfully grasp the branch and noisily haul it across the room. Turning a robot into a good teammate can be difficult, because it can be tricky to find the right amount of autonomy. Too little and it would take most or all of the focus of one human to manage one robot, which may be appropriate in special situations like explosive-ordnance disposal but is otherwise not efficient.

Too much autonomy and you'd start to have issues with trust, safety, and explainability. And if they need help, they fall back on us. RoMan is not likely to find itself out in the field on a mission anytime soon, even as part of a team with humans.

It's very much a research platform. APPL combines different machine-learning techniques including inverse reinforcement learning and deep learning arranged hierarchically underneath classical autonomous navigation systems.

That allows high-level goals and constraints to be applied on top of lower-level programming. Humans can use teleoperated demonstrations, corrective interventions, and evaluative feedback to help robots adjust to new environments, while the robots can use unsupervised reinforcement learning to adjust their behavior parameters on the fly. The result is an autonomy system that can enjoy many of the benefits of machine learning, while also providing the kind of safety and explainability that the Army needs.

With APPL, a learning-based system like RoMan can operate in predictable ways even under uncertainty, falling back on human tuning or human demonstration if it ends up in an environment that's too different from what it trained on. It's tempting to look at the rapid progress of commercial and industrial autonomous systems autonomous cars being just one example and wonder why the Army seems to be somewhat behind the state of the art.

But as Stump finds himself having to explain to Army generals, when it comes to autonomous systems, "there are lots of hard problems, but industry's hard problems are different from the Army's hard problems. Going forward, humans are likely to remain a key part of the autonomous framework that ARL is developing.

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