The introduction of generalist robot policies has enabled a single, large model to learn to perform a variety of tasks from a vast pre-training corpus. While these models often exhibit powerful zero-shot generalization capabilities, they typically require an adaptation phase to achieve sufficient performance in deploym...
Michal Korniak, M. Bagatella, Ralf Römer et al.· 0 citations
While reinforcement learning (RL) allows generalist robot policies to continually improve during deployment, the large model size of modern generalist policies, such as VLAs, poses a fundamental obstacle to effective RL improvement. In particular, their severe inference latency---which can lead to pauses or jerky movem...
Brian Zhu, Momen Khalil, E. Harrison et al.· 1 citation
In simulated and real-world robotic control settings, it is shown that in simulated and real-world robotic control settings, one can match the performance of action chunking without action chunking---by deploying an action chunking policy as an ensemble of policies with randomized delays.
Filippo Lazzati, Kyle Stachowicz, William Chen et al.· 13 citations· ⚡2
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