1 citations · 1 across the 6 of their papers we have counts for
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CAP: Codebook-Aligned Prediction for Tokenized Robot Policies
Haoran Chen, Jingtian Ji, Samuel Wheeler +2
Action tokenization converts continuous robot actions into discrete symbols that can be modeled autoregressively. However, existing tokenizer-based policies typically ignore the to…
Beyond Reconstruction: What Matters in Action Tokenization for Robot Policies?
Haoran Chen, Jingtian Ji, Samuel Wheeler +2
Autoregressive action-token policies such as vision-language-action models require action tokenizers to translate discrete token sequences into precise control actions in continuou…
CLAW: Learning Continuous Latent Action World Models via Adversarial Latent Regularization
Tewodros Ayalew, Matthew Jeung, Samuel Wheeler +5
We introduce CLAW, a fully end-to-end self-supervised framework for learning a world model jointly with continuous latent action representations directly from action-free videos. O…