5 papers
Open-World Task and Motion Planning via Vision-Language Model Generated Constraints
Nishanth Kumar, William Shen, Fabio Ramos +4
Foundation models like Vision-Language Models (VLMs) excel at common sense vision and language tasks such as visual question answering. However, they cannot yet directly solve comp…
"Set It Up": Functional Object Arrangement with Compositional Generative Models (Journal Version)
Yiqing Xu, Jiayuan Mao, Linfeng Li +4
Functional object arrangement (FORM) is the task of arranging objects to fulfill a function, e.g., "set up a dining table for two". One key challenge here is that the instructions…
Guided Exploration for Efficient Relational Model Learning
Annie Feng, Nishanth Kumar, Tomas Lozano-Perez +1
Efficient exploration is critical for learning relational models in large-scale environments with complex, long-horizon tasks. Random exploration methods often collect redundant or…
"Set It Up!": Functional Object Arrangement with Compositional Generative Models
Yiqing Xu, Jiayuan Mao, Yilun Du +3
This paper studies the challenge of developing robots capable of understanding under-specified instructions for creating functional object arrangements, such as "set up a dining ta…
Bi-Level Belief Space Search for Compliant Part Mating Under Uncertainty
Sahit Chintalapudi, Leslie Kaelbling, Tomas Lozano-Perez
The problem of mating two parts with low clearance remains difficult for autonomous robots. We present bi-level belief assembly (BILBA), a model-based planner that computes a seque…