1 citations · 1 across the 6 of their papers we have counts for
8 papers
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…
Mechanistic interpretability for steering vision-language-action models
Bear Häon, Kaylene Stocking, Ian Chuang +1
Vision-Language-Action (VLA) models are a promising path to realizing generalist embodied agents that can quickly adapt to new tasks, modalities, and environments. However, methods…
Linking vision and motion for self-supervised object-centric perception
Kaylene C. Stocking, Zak Murez, Vijay Badrinarayanan +4
Object-centric representations enable autonomous driving algorithms to reason about interactions between many independent agents and scene features. Traditionally these representat…
Stability and Robustness of a Hybrid Control Law for the Half-bridge Inverter
Gabriel E. Colón-Reyes, Kaylene C. Stocking, Duncan S. Callaway +1
Hybrid systems combine both discrete and continuous state dynamics. Power electronic inverters are inherently hybrid systems: they are controlled via discrete-valued switching inpu…
Discretizing Dynamics for Maximum Likelihood Constraint Inference
Kaylene C. Stocking, David L. McPherson, Robert P. Matthew +1
Maximum likelihood constraint inference is a powerful technique for identifying unmodeled constraints that affect the behavior of a demonstrator acting under a known objective func…
Maximum Likelihood Constraint Inference from Stochastic Demonstrations
David L. McPherson, Kaylene C. Stocking, S. Shankar Sastry
When an expert operates a perilous dynamic system, ideal constraint information is tacitly contained in their demonstrated trajectories and controls. The likelihood of these demons…