3 papers
cs.RO2026
MOMASPLAT: Dynamic Novel-View Demonstration Augmentation for Mobile Manipulation
Sateesh Kumar, Arnav Balaji, Arpit Bahety +3
Visuomotor policies trained through imitation learning are highly sensitive to changes in camera viewpoint. This is particularly problematic for mobile manipulation (MoMa), where r…
cs.RO2025
Searching in Space and Time: Unified Memory-Action Loops for Open-World Object Retrieval
Taijing Chen, Sateesh Kumar, Junhong Xu +3
Service robots must retrieve objects in dynamic, open-world settings where requests may reference attributes ("the red mug"), spatial context ("the mug on the table"), or past stat…
cs.RO2025
COLLAGE: Adaptive Fusion-based Retrieval for Augmented Policy Learning
Sateesh Kumar, Shivin Dass, Georgios Pavlakos +1
In this work, we study the problem of data retrieval for few-shot imitation learning: selecting data from a large dataset to train a performant policy for a specific task, given on…