User Case-Mobile ALOHA Mobile ALOHA based on

Introduce 𝐌𝐨𝐛𝐢𝐥𝐞 𝐀𝐋𝐎𝐇𝐀🏄 -- Learning!
With 50 demos, our robot can autonomously complete complex mobile manipulation tasks:
cook and serve shrimp 🦐
call and take an elevator 🛗
store a 3 Ibs pot to a two-door cabinet
push 5 consecutive chairs
rinse pan using a water faucet
play high fives with people
Co-led by Tony Z. Zhao, Chelsea Finn
Our robot can consistently handle these tasks, succeeding:
9 times in a row for Wipe Wine
5 times for Call Elevator
robust against distractors for Use Cabinet
extrapolate to chairs unseen during training
How do we achieve this with only 50 demos? The key is to co-train imitation learning algorithms with static ALOHA data. We found this to consistently improve performance, especially for tasks that require precise manipulation.
Co-training (1) improves the performance across all tasks, (2) is compatible with ACT, Diffusion Policy and VINN, (3) is robust to different data mixtures.
We open-source all the software and data of Mobile ALOHA!
Project Website 🛜: lnkd.in/gE6A43fR
Code for Imitation Learning 🖥️: lnkd.in/gDCmgy_E
Data 📊: lnkd.in/gCJJtmvT
#AgileXRobotics #AI #UGV #AGV #Tracer #MobileALOHA

[@MooYimYai]

Why can't we see the whole robot? Is it being controlled by someone? Time lapse and quick zooming in and out induces motion sickness too

[@learningwithjannatulferdus7898]

How much this price?

[@learningwithjannatulferdus7898]

How to make it?