I've been reading about reinforcement learning lately but got stuck on how it's actually applied to robotics. Like, machines make decisions based on rewards, but how does that translate to physical movement or tasks? Is there a standard approach for designing reward functions, or is it more trial and error? Any insights from folks who've dived deeper into this?
Reinforcement Learning in robotics: how does it work?
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What if, when designing a reward function for robotics, you don't get something as straightforward as "reaching the goal"? For example, when you tell a humanoid robot to "pick up a box and place it on the table," simply saying "if the box is in your hand, +10 points" isn't enough. There are finer details like avoiding obstacles with its arm while reaching, holding the object properly, and not taking a longer path while carrying it to the table. How do you incorporate all of this into the reward function? Is there a standard formula, or is each use case a separate puzzle?