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Specifying reward functions for robots is one of the hardest things about reinforcement learning. Robot rewards often need to be very detailed; metrics like progress can be ill-defined and hard to estimate. This leads to most robot learning defaulting to sparse rewards or simple preference learning. But naive preference...

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RoboPapers

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13,768 Aufrufe • vor 4 Tagen •via X (Twitter)

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Profilbild von Nghẹo | DOPE
Nghẹo | DOPEvor 3 Tagen

Sparse rewards often hide the behaviors we actually want

Profilbild von Muhammad Ahmed
Muhammad Ahmedvor 3 Tagen

A preference like 'better trajectory' hides the reason for choosing it. Being able to say 'less force' or 'fewer unnecessary motions' gives robot learning feedback that's much closer to what we actually care about.

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Ilir Aliu

46,285 Aufrufe • vor 1 Jahr

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Khurram Javed

52,110 Aufrufe • vor 9 Monaten

Robots, unfortunately, tend to be expensive. And finding a robot that’s both capable of performing a wide variety of mobile manipulation tasks, and is affordable and “hackable”, is extremely difficult. Many different problems need to be addressed, from arm control to navigation to integrating your data collection strategy into hardware design. This can make it difficult for all but the most well-funded teams to “scale” real-world robotics research. Fortunately, the team behind Build Your Own Robot has a solution. Manan Anjaria, Mahi Shafiullah 🏠🤖,Jeff Cui, and Enes Erciyes joined us to talk about how they build a fully open-source mobile manipulator out of off-the-shelf parts, which has humanlike range of motion, and can perform a wide variety of tasks, all while being only roughly $10,000 to build. Watch Episode 71 of RoboPapers, with Michael Cho - Rbt/Acc and Chris Paxton, today to learn more!
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RoboPapers

34,151 Aufrufe • vor 5 Monaten

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15,078 Aufrufe • vor 11 Tagen