It's $1450 if you discount the construction time, as ever. Which ordinarily wouldn't be worth commenting on, but in this case it means rewinding 12 motors which just sounds like an exercise in tedium and hand pain.
Only because they didn't know how to ask the vendor to do it for them.
I guarantee this vendor would be delighted to make them to spec at a 1ku volume, max. Rewinding isn't even a meaningful SKU distinction or line retool, it's a configuration parameter.
There are inventor programs that'll literally ship you to Shenzhen to build connections to manufacturing sites and even provide you with a liaison, etc. I only know this because I was once in a program that did exactly this.
It doesn't have to stall to stand still. Or squat, at least. With that leg layout it can safely rest against its backstops when the motors switch off. The drive motors, anyway. The hip motors probably still need to hold vertical balance, but that's intermittent, not a stall load.
The layout is doable with hobby servos, but you'd need to patch in current sensing for that bit of the feedback. It's not terribly difficult conceptually but it's an extra complication that most servo power distribution boards don't give you.
You can also strap a capstan to the servo axle, if that's your thing. I've prototyped that myself in the past. You can go surprisingly far with an FDM printer, an SG90, and some dyneema bowstring. One thing I haven't tried is modding one for continuout rotation to get around the way the capstan drive limits the output angle you can achieve - I was happy reducing from ~180deg to ~45deg for what I was doing - but that's relatively well-trodden ground. Might pull that project out of the storage box it's languishing in at some point.
Naively it feels like the improvements to resistive losses ought to be so dramatic from this that we must already be at some sort of equilibrium position. Double the voltage and divide the resistive losses by 4 - that's neither a trivial gain nor seemingly difficult to achieve? We're not talking kVs here.
It is, but you don't have to do it at every joint all the time. If you lock your knees your front and back thigh muscles don't have to work on balancing in that axis at all.
I really want to build one of those, they look great fun. (specifically in context of the article I want to see what happens if you lower the CG. Harder balance problem, but might reduce some of the instantaneous torques)
Some of it was pre-computed. The middle layer, if you like. The Boston Dynamics group had walking gait of a sort nailed in the 80's; the trotting-on-the-spot that BigDog did was essentially a continuation of those mechanics and that's all based on a conceptually simple balance problem which is intrinsically reactive and not pre-planned. So that's what was going on at the lowest level.
At the top level you have the actual environment, with those meme videos of the robot trotting through a car park, getting kicked off balance, and recovering. The whole point of those tests was to demonstrate how robust their tech was to non-precomputed disturbances.
And between the two you've got the direction and planning layer, telling the robot to go from A to B with some set of suitably convoluted parameters that nobody but the operators would have understood. That planning layer might do all sorts of pre-computation and simulation but it needs to do it in the context of a noisy and possibly adversarial environment. That's equally true for Atlas as much as it was for BigDog, even when there's nobody actually kicking it. What I suspect the precompute and simulation is doing at that layer is a) checking for physical viability of the requested route, and b) parameter tuning in response to sensor readings over a number of runs. Not telling the robot the exact sequence of motions. But I'm nowhere near those teams (oh, I wish) to comment on whether that's true - maybe someone else round here is.
Right. Early thinking on this was zero moment point (ZMP) control. The zero moment point is where, if you land from a stride there, you continue going in the same direction at the same speed. You can speed up by displacing the landing point backwards a bit, and slow down by displacing it further, and turn by displacing it left or right.
When a foot or feet are in contact with the ground, the goal is to stabilize the posture. When in flight, the goal is to hit the zero moment point.
Reactive controllers do that. The planner picks the next landing point.
ZMP robots tend to start moving by running in place, then slowly transition to fast forward motion. You see a lot of robot videos like that.
The next step up is to control takeoff. Bend knees, fall forward, launch. Launch angle is a planning problem. Speed and direction changes become much faster. Basic gymnastics are almost within reach. The planner controls launch angle and landing point, and the lower level reactive controls do the millisecond-level control.
It's possible to do all that from first principles of classical dynamics. That level of performance was reached before machine learning. That's what you're looking at in older BD videos.
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