activity
20192025
most citedOpen X-Embodiment: Robotic Learning Datasets and RT-X Models

103 citations · 371 across the 33 of their papers we have counts for

collaborators
Showing 2024Show all

11 papers · 1 filter

cs.RO2024

Maximizing Alignment with Minimal Feedback: Efficiently Learning Rewards for Visuomotor Robot Policy Alignment

Ran Tian, Yilin Wu, Chenfeng Xu +3

Visuomotor robot policies, increasingly pre-trained on large-scale datasets, promise significant advancements across robotics domains. However, aligning these policies with end-use…

cs.CV2024

X-Drive: Cross-modality consistent multi-sensor data synthesis for driving scenarios

Yichen Xie, Chenfeng Xu, Chensheng Peng +6

Recent advancements have exploited diffusion models for the synthesis of either LiDAR point clouds or camera image data in driving scenarios. Despite their success in modeling sing…

cs.CV2024

DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes

Chensheng Peng, Chengwei Zhang, Yixiao Wang +6

We present DeSiRe-GS, a self-supervised gaussian splatting representation, enabling effective static-dynamic decomposition and high-fidelity surface reconstruction in complex drivi…

cs.CV2024

CompGS: Unleashing 2D Compositionality for Compositional Text-to-3D via Dynamically Optimizing 3D Gaussians

Chongjian Ge, Chenfeng Xu, Yuanfeng Ji +6

Recent breakthroughs in text-guided image generation have significantly advanced the field of 3D generation. While generating a single high-quality 3D object is now feasible, gener…

cs.CV2024

TrajSSL: Trajectory-Enhanced Semi-Supervised 3D Object Detection

Philip Jacobson, Yichen Xie, Mingyu Ding +4

Semi-supervised 3D object detection is a common strategy employed to circumvent the challenge of manually labeling large-scale autonomous driving perception datasets. Pseudo-labeli…

cs.RO2024★ 1 cited

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…