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Yitang Li

@li_yitang • 1,315 subscribers

1st-year PhD at Stanford | Tsinghua IIIS (Yao Class) alum | Robot Learning

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Can humanoids adapt to new terrain using only flat-ground references? 🤖 Meet P-BFM! 🥳 We learn a terrain-agnostic intent space with unsupervised RL and forward-backward representations, paired with a terrain-aware policy that adapts across diverse terrains—without terrain-specific demonstrations. 📄🎬💻 Check out our paper, video, and code here! 🌐

Can humanoids adapt to new terrain using only flat-ground references? 🤖 Meet P-BFM! 🥳 We learn a terrain-agnostic intent space with unsupervised RL and forward-backward representations, paired with a terrain-aware policy that adapts across diverse terrains—without terrain-specific demonstrations. 📄🎬💻 Check out our paper, video, and code here! 🌐

13,580 Aufrufe • vor 16 Tagen

Meet BFM-Zero: A Promptable Humanoid Behavioral Foundation Model w/ Unsupervised RL👉 🧩ONE latent space for ALL tasks ⚡Zero-shot goal reaching, tracking, and reward optimization (any reward at test time), from ONE policy 🤖Natural recovery & transition

Meet BFM-Zero: A Promptable Humanoid Behavioral Foundation Model w/ Unsupervised RL👉 🧩ONE latent space for ALL tasks ⚡Zero-shot goal reaching, tracking, and reward optimization (any reward at test time), from ONE policy 🤖Natural recovery & transition

84,209 Aufrufe • vor 11 Monaten

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