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Most adversarial attacks on deep reinforcement learning (DRL) assume white-box access to the victim policy, which rarely holds in practice. This paper studies transfer-based black-box attacks on DRL: the attacker crafts observation perturbations on a white-box surrogate agent and feeds them to an unknown victim. We for...
Large-scale vision-language-action (VLA) models provide powerful priors for robot manipulation, yet adapting them to a specific deployment remains challenging. Supervised fine-tuning (SFT) on task-specific demonstrations provides a step toward deployment, but faces two persistent limitations: static data provide limite...
A data-efficient diffusion RL post-training framework - GQRM (Group Q-score Reweighted Matching), which achieves state-of-the-art cross-embodiment visual navigation performance and introduces two complementary designs: a self-bootstrapped exploration strategy with behavior perturbation that preserves the pretrained pol...
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