6 ms·

I was an intern at Rethink (back then, Heartland Robotics) 2010~2011, it was an exciting place to work at. There were ~10 employees when I joined, and ~40 when

I was an intern at Rethink (back then, Heartland Robotics) 2010~2011, it was an exciting place to work at. There were ~10 employees when I joined, and ~40 when I left. One of the early product mistaked they made was focusing way too much on low-cost. They compromised many necessary performance specs (repeatability, speed etc.) just to be able to make the robot out of plastic. They eventually pivoted to their second generation robot, Sawyer, which was made out of casted metal components. This gave the robot much better performance but I guess that it was too late.

gisely 8y ago
I would really love to hear why performance specs you mention are still necessary for robotics applications. Repeatability and speed certainly seem useful industrial robotics applications, but low cost also seems critical to enabling broader use of robots. I am particularly interested in the possibility that modern algorithms (e.g. deep reinforcement learning) can compensate for not having completely repeatable behaviors from a robot by using sensory feedback to create behaviors that reliable despite their variability. Additionally, materials with higher elasticity (e.g. plastics instead of metal) might allow some previous impossible efficiencies, e.g. by storing potential energy in the links and then releasing it at the right time. Is it just too soon for these sorts of approaches?
- blt 8y ago
  Basically, yes. Deep RL has the potential to make high accuracy/repeatability unnecessary, but it's still in the research lab setting.
- LeifCarrotson 8y ago
  Compromise on stiffness and rigidity in a servo system means the thing jitters about like a squirrel on speed. Robots need to be smooth, and there's precious little you can do in your controls to compensate for system instability. Precision comes along almost for free once you have that rigidity. They didn't get the basics right. Special sauce on top needs to have a solid foundation. And that's why I have an Aubo robot in my conference room today, and why I was able to tune in a 250kg payload 1995-vintage ABB on Tuesday, while these guys are shutting down.
  - balfirevic 8y ago
    > and why I was able to tune in a 250kg payload 1995-vintage ABB on Tuesday What does that mean?
    - markbnj 8y ago
      ABB is a major supplier of industrial robots, so I assume the poster meant installing/setting up a used 1995 model capable of handling 250kg.
      - balfirevic 8y ago
        Thanks!
      - jacobush 8y ago
        Or tune in a 250kg payload on such a robot - I can imagine the motion programming might have to be tweaked to swing right. I don't know what such a heave payload might be - maybe some heavy part to be welded.
      - deleted 8y ago
        [deleted]
    - ganonm 8y ago
      By 'tune in' he means calibrate an end effector to be mounted on the robot. This involves TCP calibration (tool center point) and specifying the centre of mass and moments of inertia. KUKA robots, for example, allow you to determine the TCP by manually posing the robot so that the tip of the end effector (tool) is at a fixed point. This is then repeated three more times from different poses (but the same tool point) so that the robot can solve for the 3 values that determine the tool's XYZ offset from the flange (mounting point at the end of the robot). The robot controller then uses this data when calculating inverse kinematics and supplying power to the axes.
  - JabavuAdams 8y ago
    As an existence proof, human limbs aren't stiff but are capable of building all the things, with jigs. So better control + jigs should be able to do what we want, but maybe this isn't productizable yet?
- YeGoblynQueenne 8y ago
  >> I am particularly interested in the possibility that modern algorithms (e.g. deep reinforcement learning) can compensate for not having completely repeatable behaviors from a robot by using sensory feedback to create behaviors that reliable despite their variability. Deep Reinforcement Learning will definitely make a huge difference in robotics. Already, ~50 years after the first implementations of RL algorithms [1] robotic systems trained with (deep) RL have now become so proficient that a robotic hand has learned to manipulate a cube [2]. Always the same cube (in terms of size, weight, etc) but a cube, nonetheless! Full-blown general object manipulation cannot be more than a hundred years away, or two! __________________ [1] I'm counting from Donald Michie's MENACE, a tick-tack-toe playing machine originally implemented in matchboxes, because of a dearth of computing hardware of sufficient power- see Rodney Brooks' excellent write-up: https://rodneybrooks.com/forai-machine-learning-explained/ https://rodneybrooks.com/forai-machine-learning-explained/ [2] OpenAI trained a robot hand to manipulate a cube with RL: https://blog.openai.com/learning-dexterity/ https://blog.openai.com/learning-dexterity/ Note that it's always the same cube. If they could train it to manipulate anything else, you can bet they'd be showing it off (and probably publishing in Nature or something).
iandanforth 8y ago
Plastic vs metal has nothing to do with it. A series elastic actuator is going to introduce error far larger than a standard harmonic gear drive motor whatever you make it out of.

gisely 8y ago

I would really love to hear why performance specs you mention are still necessary for robotics applications. Repeatability and speed certainly seem useful industrial robotics applications, but low cost also seems critical to enabling broader use of robots. I am particularly interested in the possibility that modern algorithms (e.g. deep reinforcement learning) can compensate for not having completely repeatable behaviors from a robot by using sensory feedback to create behaviors that reliable despite their variability. Additionally, materials with higher elasticity (e.g. plastics instead of metal) might allow some previous impossible efficiencies, e.g. by storing potential energy in the links and then releasing it at the right time. Is it just too soon for these sorts of approaches?

blt 8y ago
Basically, yes. Deep RL has the potential to make high accuracy/repeatability unnecessary, but it's still in the research lab setting.
LeifCarrotson 8y ago
Compromise on stiffness and rigidity in a servo system means the thing jitters about like a squirrel on speed. Robots need to be smooth, and there's precious little you can do in your controls to compensate for system instability. Precision comes along almost for free once you have that rigidity. They didn't get the basics right. Special sauce on top needs to have a solid foundation. And that's why I have an Aubo robot in my conference room today, and why I was able to tune in a 250kg payload 1995-vintage ABB on Tuesday, while these guys are shutting down.
- balfirevic 8y ago
  > and why I was able to tune in a 250kg payload 1995-vintage ABB on Tuesday What does that mean?
  - markbnj 8y ago
    ABB is a major supplier of industrial robots, so I assume the poster meant installing/setting up a used 1995 model capable of handling 250kg.
    - balfirevic 8y ago
      Thanks!
    - jacobush 8y ago
      Or tune in a 250kg payload on such a robot - I can imagine the motion programming might have to be tweaked to swing right. I don't know what such a heave payload might be - maybe some heavy part to be welded.
    - deleted 8y ago
      [deleted]
  - ganonm 8y ago
    By 'tune in' he means calibrate an end effector to be mounted on the robot. This involves TCP calibration (tool center point) and specifying the centre of mass and moments of inertia. KUKA robots, for example, allow you to determine the TCP by manually posing the robot so that the tip of the end effector (tool) is at a fixed point. This is then repeated three more times from different poses (but the same tool point) so that the robot can solve for the 3 values that determine the tool's XYZ offset from the flange (mounting point at the end of the robot). The robot controller then uses this data when calculating inverse kinematics and supplying power to the axes.
- JabavuAdams 8y ago
  As an existence proof, human limbs aren't stiff but are capable of building all the things, with jigs. So better control + jigs should be able to do what we want, but maybe this isn't productizable yet?
YeGoblynQueenne 8y ago
>> I am particularly interested in the possibility that modern algorithms (e.g. deep reinforcement learning) can compensate for not having completely repeatable behaviors from a robot by using sensory feedback to create behaviors that reliable despite their variability. Deep Reinforcement Learning will definitely make a huge difference in robotics. Already, ~50 years after the first implementations of RL algorithms [1] robotic systems trained with (deep) RL have now become so proficient that a robotic hand has learned to manipulate a cube [2]. Always the same cube (in terms of size, weight, etc) but a cube, nonetheless! Full-blown general object manipulation cannot be more than a hundred years away, or two! __________________ [1] I'm counting from Donald Michie's MENACE, a tick-tack-toe playing machine originally implemented in matchboxes, because of a dearth of computing hardware of sufficient power- see Rodney Brooks' excellent write-up: https://rodneybrooks.com/forai-machine-learning-explained/ https://rodneybrooks.com/forai-machine-learning-explained/ [2] OpenAI trained a robot hand to manipulate a cube with RL: https://blog.openai.com/learning-dexterity/ https://blog.openai.com/learning-dexterity/ Note that it's always the same cube. If they could train it to manipulate anything else, you can bet they'd be showing it off (and probably publishing in Nature or something).

iandanforth 8y ago

Plastic vs metal has nothing to do with it. A series elastic actuator is going to introduce error far larger than a standard harmonic gear drive motor whatever you make it out of.