I think this paper is a nice read but I thought it is worth clearing up some misconceptions about reflected inertia and gear ratio.
First of all, if you keep output torque constant, reflected inertia is actually independent of gear ratio (if you ignore gear friction and mass). You can try different combinations of gear ratio and rotor inertia, but if you have output torque as a constant, regardless of the combination you picked, you'll end up with the same reflected inertia. This is a pretty important intuition to build, and it's also the reason why the reflected inertia between outrunner actuators and inrunner actuators is pretty much the same for the same torque (but inrunners have slightly lower because of differences in mechancial implementation). And also why linear actuators roughly have the same reflected inertia as a rotary actuator with the same joint torque. It's a little harder to compare reflected inertia of linears vs rotaries because reflected inertia in a 4-bar linkage is non linear, and also depends if you invert the gear train or not :)
The part I'm not sure about is that the gear ratio is decreased by an order of magnitude (from 288:1 to 15:1) and to compensate for the loss in torque, they use an axial flux motor. Axial flux motors are cool but they don't give you THAT much more torque.
Also, torque transparency can be decent up to gear ratios of 100 or even higher. Gearbox efficiency motor side losses dictate an actuator's ability to sense output torque, and gear ratio is a just multiplier of that effect.
Ie high gearbox efficiency means you can get away with higher gear ratios without sacrificing proprioception. Involute gear teeth are very efficient, and given an efficient gear tooth design, gearbox efficiency is determined by how many stages you have, not necessarily gear ratio. Single-stage = very good, two-stage = pretty good, three-stage = decent. And the converse is true as well: low efficiency gearing, like harmonic drives, means that you will always need an external torque sensor, regardless of gear ratio. (Not only do harmonic drives have low efficiency, but also their efficiency is non linear with speed and torque)
It's easy to point fingers at gear ratio as the parameter to blame for sim2real gaps. However a well designed actuator with a 30:1 gear ratio and a smaller/lighter motor often times outperforms a 15:1 actuator with a larger motor if you also care about total mass, thermal performance, and battery life. But I do think for a hand that only needs to do light dexterous work like origami, going down the low-ratio route is a sure-fire way to make your models happy
Why does manipulation lag so far behind locomotion? New post on one piece we don't talk about enough: The gearbox. The Gap You've probably seen those dancing humanoid robots from Chinese New Year. Locomotion isn't entirely solved; but clearly it's on a trajectory. But we haven't seen anything close for manipulation. 𝗪𝗵𝘆? When sim-to-real transfer fails, the instinct is to blame the algorithm. Train bigger networks. Crank up domain randomization. Those approaches have made real progress; we don't deny that. But we started wondering: are we treating the symptom or the disease? The Hardware Bottleneck: Fingers are too small for powerful motors. So most hands use massive gearboxes (200:1, 288:1) to get enough torque. But those gearboxes break everything manipulation needs:
• Stiction and backlash are complex to simulate. Policies trained on smooth physics hallucinate when they hit that reality.
• Reflected inertia scales as N². At large gear ratio, the finger hits with sledgehammer momentum.
• Friction blocks force information. The hand becomes blind.
And they're the first thing to break. What we are trying to build at Origami, we cut the gear ratio from 288:1 to 15:1 using axial flux motors and thermal optimization. The transmission becomes more transparent: backdrivable, low friction, forces propagate to motor current. Early signs are encouraging. Still running quantitative benchmarks. Why Interactive? I love how Science Center uses interactive devices to explain complex ideas. I want to borrow this concept and help people understand the hard problems in robotics better visually. The post has demos where you can toggle friction, slide gear ratios, watch the sim-to-real gap widen in real-time. What's inside:
• Interactive demos (friction curves, N² scaling, contact patterns)
• Comparison table: 14 robot hands by sim-to-real gap and force transparency
• The math behind why low-ratio matters
Read it here:
origami-robotics.com/blog/de… We're not claiming we've solved dexterity. The deadlock has many pieces. But we think this one's foundational. Curious what you think.