Paper clips: when picking up a paperclip, humans inelegantly plop their hand down into a tub of clips. We don’t aim for one, we just aim for the whole jar. Once we’re there, we roll our hand around, taking advantage of multiple articulated end points, picking up one or even a dozen, and then quickly finding a grip on just one and releasing the rest. Robots are typically programmed to pick up just one object at time. This task would take many retries, to the irritation of the boss.
Unstick a label from itself: sticking labels to things is an important aspect of many jobs. Such labels peel off easily from skin and not so easily from metal and plastic. While a machine perfectly aligned to repeatedly stick labels to the same object day after day has no trouble, a machine at a desk job might receive a myriad of different object and label sizes – it, like you, might take a couple of tries to get it lined up correctly, but, it, unlike you, will struggle to overcome misaligned attempts.
Pick up a piece of paper, fallen in a tight space: if paper falls between the baseboard and your desk leg, you might not immediately be able to reach it. Often, what’s required is an initial, unsuccessful reach, and then a twisted, contorted accommodation, your scapula sliding down your back, your pinkie finger eking out sideways just a bit as you lean into your forearm, finding the squishy muscle giving just a bit, so that you gain some additional clearance around a radiator and … there! You’ve got it. Today’s robots wouldn’t have all these extra choices that allow humans to navigate tight spaces. These machines typically have rigid links that only rotate – not translate relative to one another as bones can, ever-so-slightly, do. If the robot dropped an important piece of paper and were unable to retrieve it, or it left littered pieces of paper all around its desk, I doubt it would keep its job.
Appropriately laugh at an inappropriate joke: we’ve all been there. Your boss or coworker makes an off-colour joke. Regardless of how you decide to react, you have to walk a fine line if you want to stay in their good graces. You can, of course, choose not to laugh. Or, on the other extreme, you can give a hearty bellylaugh. These two options are probably both liable to put you in a difficult position. On the one hand, not laughing at all might embarrass your boss; on the other, laughing too hard could give the impression that you approve of the inappropriate joke. Therefore, you will probably elect to find something inbetween. This requires leveraging your full mechanical complexity to indicate shades of approval and disapproval, simultaneously. Maybe you give a forced laugh with a disapproving eye and a half-smile, letting your boss know that you understand the joke, know it’s not appropriate, but also aren’t going to tell anyone about it. It creates a kind of social bond that can be very important at work, utilising a behaviour that robots such as my colleague’s cannot emulate.
That robots can ‘do backflips’ is an impressive feat. At first glance, the backflip seems like the peak of physical performance: so few people can do one. On the other hand, catching a set of oddly shaped keys, thrown without warning, splayed in an awkward shape, flying across a myriad of backgrounds – maybe in the rain at night by a drunken friend with poor coordination – is a task that almost any adult human could do but that few, if any, robots could complete.
By all means, keep watching in awe as roboticists continue to improve the mechanical feats of machines. But know that you yourself (yes, even you with a desk job who shirks the weekly exercise class), you do incredible things that we do not yet understand – that we do not yet even value.
Amy LaViers is a certified movement analyst, via the Laban/Bartenieff Institute of Movement Studies in New York, and director of the Robotics, Automation, and Dance (RAD) Lab at the University of Illinois at Urbana-Champaign. She is the co-editor, with Magnus Egerstedt, of Controls and Art: Inquiries at the Intersection of the Subjective and the Objective (2014).
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Source: Work Place Insight
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