4 accepted papers
Recent advancements in Large Language Models (LLMs) have sparked a revolution across many research fields. In robotics, the integration of common-sense knowledge from LLMs into task and motion planning has drastically advanced the field by unlocking unprecedented levels of context awareness. Despite
Existing evaluation paradigms for Autonomous Vehicles (AVs) face critical limitations. Real-world evaluation is often challenging due to safety concerns and a lack of reproducibility, whereas closed-loop simulation can face insufficient realism or high computational costs. Open-loop evaluation, whil…
Designing algorithms is cumbersome and error-prone. This, among other things, has increasingly led to efforts to extend or even replace designing algorithms with machine learning models. While previous research has demonstrated that some machine learning models possess Turing-completeness, the findi…
The difficulty in finding long-term planning policies for a mobile robot increases when operating in crowded and dynamic environments. State-of-the-art approaches do not consider cues of human-robot-shared dynamic environments. Aiming to fill this gap, we present a novel Human-Flow-Aware Guided Hier