this post was submitted on 24 Nov 2023
1 points (100.0% liked)
Machine Learning
1 readers
1 users here now
Community Rules:
- Be nice. No offensive behavior, insults or attacks: we encourage a diverse community in which members feel safe and have a voice.
- Make your post clear and comprehensive: posts that lack insight or effort will be removed. (ex: questions which are easily googled)
- Beginner or career related questions go elsewhere. This community is focused in discussion of research and new projects that advance the state-of-the-art.
- Limit self-promotion. Comments and posts should be first and foremost about topics of interest to ML observers and practitioners. Limited self-promotion is tolerated, but the sub is not here as merely a source for free advertisement. Such posts will be removed at the discretion of the mods.
founded 1 year ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
I’m guessing inputs are the pose (x y z, rx ry rz) and the outputs are your joints.
Two things: unless you are just trying to solve for the final position, it may be smart to add the initial pose. So if the trajectory matters for purposes of avoiding obstacles then knowing the start position and maybe the obstacles in configuration space could be helpful input to the network. That being said, some sort of sequence model where you encode the goal with an mlp and then a decoder for the trajectory would be cool. Id you just want the final joints then an mlp is fine.
Some people use reinforcement learning in this setting too but I tend to think that’s overkill.
As others have said too: you could always use a traditional algorithm like some variant of RRTs to solve this too as it’s mostly a “done” problem for traditional robotics.