We have 3 of 42 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
It is found that though continual learning methods help, fine-tuning reduces robustness across pretrained models, suggesting that starting with the strongest foundation model is not necessarily the best approach for performance on specialist tasks.
This paper proposes an alternative approach in which computationally intensive VLM queries are made during train-time to learn a lightweight latent memory that can be efficiently queried at deployment time, and demonstrates that the workspace tokens are not only more lightweight, but also lead to better policy performa...
Learning from Hindsight is presented, which brings hindsight relabeling to RL post-training of VLAs by scoring failed rollouts against the tasks they actually achieved, and achieves 5 times improvement in sample efficiency, and outperforms a dense progress-reward baseline on out-of-distribution LIBERO-PRO tasks.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
