4 accepted papers
We introduce Select2Plan (S2P), a novel training-free framework for high-level robot planning that leverages off-the-shelf Vision-Language Models (VLMs) for autonomous navigation. Unlike most learning-based approaches that require extensive task- specific training and large-scale data collection, S2…
We introduce Select2Plan (S2P), a novel training-free framework for high-level robot planning that leverages off-the-shelf VLMs for autonomous navigation. Unlike most learning-based approaches that require extensive task-specific training and large-scale data collection, S2P overcomes the need for f
Synthetic data generation (SDG) has become a popular approach to release private datasets. In SDG, a generative model is fitted on the private real data, and samples drawn from the model are released as the protected synthetic data. While real-world datasets usually consist of multiple tables with p…
This work proposes a fast deployment pipeline for visually-servoed robots which does not assume anything about either the robot - e.g. sizes, colour or the presence of markers - or the deployment environment. Specifically, we apply a learning based approach to reliably estimate the pose of a robot i…