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by imiric
889 days ago
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I get that, but yeah, with ML it would be a matter of training it on raw data: objects, materials, physical properties and behaviors, etc. And then "intuition" would arise from this knowledge, and its own experience from reinforced learning. It's the same problem as implementing self-driving in vehicles, just applied to a different domain. I'm not downplaying the difficulty, of course, but pointing out that this type of automation wouldn't be feasible if we'd have to classically program every scenario the robot is likely to encounter. |
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We don't even know if "intuition" would arise from the knowledge you claim, we don't know how that model would work, and even before that, collecting all the data (not to speak of availability of all the sensors) is a vastly more complex than even what ChatGPT or any LLM model data collection would ever be.
>it's own experience from reinforcement learning
This is a common mistake often heard from CS -> ML(RL) -> robotics transition folks. Reward function is given for free in RL, but in the real world, estimating the reward is a complex problem in its self. That's why RL on robotics have mostly seen success in quadrupedal locomotion; the reward function is simple (forward velocity, calculated from IMU), but how would you calculate a reward function in 30Hz+ for a simple task such as "chop onion and put it in the pan"? If you can construct the reward function for that task, most likely, you already have all the world-states available and might as well skip RL and do something else with that, such as Model-predictive control.
As for intuition, see: https://en.wikipedia.org/wiki/Moravec%27s_paradox