5 accepted papers
We introduce DavIR, a model-based data selection method for post-training Large Language Models. DavIR generalizes Reducible Holdout Loss to core-set selection problem of causal language modeling, and quantifies the learnability of a given datum with respect to a pre-trained LLM based on relative re…
Large Language Models (LLMs) are now transforming the way robots learn to work in unpredictable environments, such as homes or small enterprises. A growing number of approaches are combining LLMs with Behavior Trees (BTs). Not only do user commands need to be interpreted into BTs that contain the ta
Terrain classification is essential for traversability estimation and planning of unmanned ground vehicles (UGVs) in complex environments. Most existing approaches utilize fully supervised learning to classify terrains based on either exteroceptive or proprioceptive sensor modalities. However, visio
Large deep learning models have achieved impressive performance across a range of applications. However, their large memory requirements, including parameter memory and activation memory, have become a significant challenge for their practical serving. While existing methods mainly address parameter…
Large Language Models (LLMs) have been widely utilized to perform complex robotic tasks. However, handling external disturbances during tasks is still an open challenge. This paper proposes a novel method to achieve robotic adaptive tasks based on LLMs and Behavior Trees (BTs). It utilizes ChatGPT t…