1 citations · 2 across the 4 of their papers we have counts for
5 papers
KiRoSe: Using Kinematic Data for Efficient Segmentation of Robot Demonstrations
Shuangyu Xie, Kaiyuan Chen, Muhammad Zubair Irshad +3
Segmenting robot demonstration data into disjoint temporal and semantic subsegments is valuable for learning, cleaning, retrieval, and analysis. Segmentation is often performed man…
Real2Render2Real: Scaling Robot Data Without Dynamics Simulation or Robot Hardware
Justin Yu, Letian Fu, Huang Huang +5
Scaling robot learning requires vast and diverse datasets. Yet the prevailing data collection paradigm-human teleoperation-remains costly and constrained by manual effort and physi…
Persistent Object Gaussian Splat (POGS) for Tracking Human and Robot Manipulation of Irregularly Shaped Objects
Justin Yu, Kush Hari, Karim El-Refai +6
Tracking and manipulating irregularly-shaped, previously unseen objects in dynamic environments is important for robotic applications in manufacturing, assembly, and logistics. Rec…
Language-Embedded Gaussian Splats (LEGS): Incrementally Building Room-Scale Representations with a Mobile Robot
Justin Yu, Kush Hari, Kishore Srinivas +9
Building semantic 3D maps is valuable for searching for objects of interest in offices, warehouses, stores, and homes. We present a mapping system that incrementally builds a Langu…
RoVi-Aug: Robot and Viewpoint Augmentation for Cross-Embodiment Robot Learning
Lawrence Yunliang Chen, Chenfeng Xu, Karthik Dharmarajan +6
Scaling up robot learning requires large and diverse datasets, and how to efficiently reuse collected data and transfer policies to new embodiments remains an open question. Emergi…