3 accepted papers
Embodied agents face a fundamental limitation: once deployed in real-world environments, they cannot easily acquire new knowledge to improve task performance. In this paper, we propose Dejavu, a general post-deployment learning framework that augments a frozen Vision-Language-Action (VLA) policy wit
Zero-Shot Object Goal Navigation (ZSON) is an important task for robots. While Multimodal Large Language Models (MLLMs) have empowered robots with significant semantic reasoning capabilities, current RGB-only navigation methods still struggle to align high-level discrete logic with low-level continu…
The task-conditional model is a distinctive stream for efficient multi-task learning. Existing works encounter a critical limitation in learning task-agnostic and task-specific representations, primarily due to shortcomings in global context modeling arising from CNN-based architectures, as well as…