4 papers
Object Search in Partially-Known Environments via LLM-informed Model-based Planning and Prompt Selection
Abhishek Paudel, Abhish Khanal, Raihan I. Arnob +2
We present a novel LLM-informed model-based planning framework, and a novel prompt selection method, for object search in partially-known environments. Our approach uses an LLM to…
Multi-Robot Learning-Informed Task Planning Under Uncertainty
Abhish Khanal, Abhishek Paudel, Hung Pham +1
We want a multi-robot team to complete complex tasks in minimum time where the locations of task-relevant objects are not known. Effective task completion requires reasoning over l…
Effective Task Planning with Missing Objects using Learning-Informed Object Search
Raihan Islam Arnob, Max Merlin, Abhishek Paudel +3
Task planning for mobile robots often assumes full environment knowledge and so popular approaches, like planning via the PDDL, cannot plan when the locations of task-critical obje…
Using Contextual Information for Sentence-level Morpheme Segmentation
Prabin Bhandari, Abhishek Paudel
Recent advancements in morpheme segmentation primarily emphasize word-level segmentation, often neglecting the contextual relevance within the sentence. In this study, we redefine…