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Philipp Wu

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Anya Rossi

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🎉Excited to share a fun little hardware project we’ve been working on. GELLO is an intuitive and low cost teleoperation device for robot arms that costs less than $300. We've seen the importance of data quality in imitation learning. Our goal is to make this more accessible 1/n

🎉Excited to share a fun little hardware project we’ve been working on. GELLO is an intuitive and low cost teleoperation device for robot arms that costs less than $300. We've seen the importance of data quality in imitation learning. Our goal is to make this more accessible 1/n

162,934 次观看 • 2 年前

Introducing Masked Trajectory Modeling (MTM), a new general-purpose framework for sequential decision making. A single transformer trained with MTM can exhibit multiple capabilities by simply choosing different masking patterns at inference time. Accepted at ICML 2023. 🧵👇

Introducing Masked Trajectory Modeling (MTM), a new general-purpose framework for sequential decision making. A single transformer trained with MTM can exhibit multiple capabilities by simply choosing different masking patterns at inference time. Accepted at ICML 2023. 🧵👇

93,121 次观看 • 3 年前

It's been over 2 years since we first released GELLO. We’ve updated the project with 3 new GELLOs to control the YAM, ARX and similar arms! Enables easy data collection for policy training (ie finetuned Pi0!). All open source contributions from 劉添慶 and hmahesh007!

It's been over 2 years since we first released GELLO. We’ve updated the project with 3 new GELLOs to control the YAM, ARX and similar arms! Enables easy data collection for policy training (ie finetuned Pi0!). All open source contributions from 劉添慶 and hmahesh007!

13,495 次观看 • 10 个月前

New work with Yide Shentu! We present Latent Code as Bridges🌉 or LCB. We leverage LLMs for robotics by finetuning the LLM to output latent embeddings to control the lower level policy. This hierarchical approach enables end2end training while maintaining the advantages of LLMs!

New work with Yide Shentu! We present Latent Code as Bridges🌉 or LCB. We leverage LLMs for robotics by finetuning the LLM to output latent embeddings to control the lower level policy. This hierarchical approach enables end2end training while maintaining the advantages of LLMs!

21,085 次观看 • 2 年前

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