Zipeng Fu
353
posts
@zipengfu
Palo Alto, CA
Joined February 2014
Pinned
Mobile ALOHA's hardware is very capable. We brought it home yesterday and tried more tasks! It can: - do laundry👔👖 - self-charge⚡️ - use a vacuum - water plants🌳 - load and unload a dishwasher - use a coffee machine☕️ - obtain drinks from the fridge and open a beer🍺 - open
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These are not legs. These are active suspension systems.
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The first sim2real RL-based controller for Fourier GR1 to my knowledge. Using ROA. Congrats!
Smooth behaviors is vital for successful sim2real transfer of RL policies. This is often achieved with smoothness rewards or low-pass filters, which are not easily differentiable and tend to require tedious tuning. We introduce Lipschitz-Constrained Policies (LCP), a simple and
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Introduce DoggyBot🐕series: quadrupeds can also do manipulation. It's a fruitful 4yr journey working on robot dogs from walking, to parkour, to now useful agility. We open-sourced everything. Hope others can build on our code and start a series of projects named "xxx DoggyBot".
Introducing Helpful DoggyBot🐕, a legged mobile manipulation system: - A quadruped with a mouth - Agile whole-body skills like climbing and tilting - Open-world object fetching using VLMs - No real-world training data required!
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- RL in sim to train a task-agnostic whole-body controller - imitation learning in real for task-specific policies Advancing robotics demands a thoughtful integration of solutions, rather than overreliance on any single approach Glad to witness this year-long project completed!
I’ve been training dogs since middle school. It’s about time I train robot dogs too 😛 Introducing, UMI on Legs, an approach for scaling manipulation skills on robot dogs🐶It can toss, push heavy weights, and make your ~existing~ visuo-motor policies mobile!
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