3 ms·
Maybe I am missing something but fundamentally you can "learn" in the ML sense with any sensor and effector. Examples of cheap sensors: MEMS microphone, camera
by contingencies 7y ago
Maybe I am missing something but fundamentally you can "learn" in the ML sense with any sensor and effector.
Examples of cheap sensors: MEMS microphone, camera, voltage sensor, mass sensor, distance sensor, multi-axis position sensors (gyroscope/magnetometers).
Examples of effectors: Any kind of motor, solenoid, LEDs, etc.
If you want to constrain the question to 3D motion, here is a suggestion - hack the controller of any existing RC car platform. Add overhead position sensing within a fixed arena added via external camera. Maybe add a MEMS microphone (USD$3) or position sensor (~USD$10) to verify airtime/orientation. ML problem #1: Add a ramp. Try to get it to jump highest (longest time airborne). ML problem #2: Same with a power efficiency metric. ML problem #3: Same with a time efficiency metric applied to navigation from a random start point and orientation. ML problem #4: Motor noise vs. jump height optimization.
- cbames89 7y agoByron Boot's lab at Georgia Tech does a lot of interesting work with larger-scale R/C vehicles. That being said, by adding more degrees of freedom you're making the configuration space of the robot higher. In many cases, higher dimension configuration spaces are more difficult for traditional sampling-based approaches. Learning approaches may be able to bias their solutions to avoid large sections of this configuration space.