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Simeon Adebola

8 accepted papers

2026

OXE-AugE: A Large-Scale Robot Augmentation of OXE for Scaling Cross-Embodiment Policy Learning

Guanhua Ji, Harsha Polavaram, Lawrence Yunliang Chen, Sandeep Bajamahal, Zehan Ma, Simeon Adebola +2
ICML 2026spotlight

Large and diverse datasets are needed for training generalist robot policies that have potential to control a variety of robot embodiments--robot arm and gripper combinations--across diverse tasks and environments. As re-collecting demonstrations and retraining for each new hardware platform are pro…

Cited by 0Source ↗Scholar ↗
2026

RoboVista: Evaluating Vision Language Models for Diverse Robot Applications

Shuangyu Xie, Kaiyuan Chen, Ziyang Chen, Simeon Adebola, Yixuan Huang, Zehan Ma +5
RSS 2026poster

Diverse applications for robotics, such as industry and agriculture, require robots to operate across various embodiments, changing visual conditions, and complex planning. Vision–Language Models (VLMs) offer a promising foundation for general-purpose and interpretable robotic reasoning. Aligning VL…

Cited by 0Source ↗Scholar ↗
2025

Botany-Bot: Digital Twin Monitoring of Occluded and Underleaf Plant Structures with Gaussian Splats

Simeon Adebola, Chung Min Kim, Justin Kerr, Shuangyu Xie, Prithvi Akella, Jose Luis Susa Rincon +2
IROS 2025

Commercial plant phenotyping systems using fixed cameras cannot perceive many plant details due to leaf occlusion. In this paper, we present Botany-Bot, a system for building detailed “annotated digital twins” of living plants using two stereo cameras, a digital turntable inside a lightbox, an indus

Cited by 0Source ↗code ↗Scholar ↗
2024

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration

Abby O'Neill, Abdul Rehman, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee +273
ICRA 2024

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for man

Cited by 910Source ↗code ↗Scholar ↗
2024

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0

Abby O’Neill, Abdul Rehman, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee +273
ICRA 2024poster

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for man…

Cited by 259Source ↗code ↗Scholar ↗
2023

Can Machines Garden? Systematically Comparing the AlphaGarden vs. Professional Horticulturalists

Simeon Adebola, Rishi Parikh, Mark Presten, Satvik Sharma, Shrey Aeron, Ananth Rao +5
ICRA 2023poster

The AlphaGarden is an automated testbed for indoor polyculture farming which combines a first-order plant simulator, a gantry robot, a seed planting algorithm, plant phenotyping and tracking algorithms, irrigation sensors and algorithms, and custom pruning tools and algorithms. In this paper, we sys…

Cited by 4Source ↗Scholar ↗
2023

FogROS2-SGC: A ROS2 Cloud Robotics Platform for Secure Global Connectivity

Kaiyuan Chen, Ryan Hoque, Karthik Dharmarajan, Edith LLontopl, Simeon Adebola, Jeffrey Ichnowski +2
IROS 2023poster

The Robot Operating System (ROS2) is the most widely used software platform for building robotics applications. FogROS2 extends ROS2 to allow robots to access cloud computing on demand. We introduce FogROS2-SGC, an extension of FogROS2 that can effectively connect robot systems across different phys…

Cited by 15Source ↗code ↗Scholar ↗
2023

FogROS2: An Adaptive Platform for Cloud and Fog Robotics Using ROS 2

Jeffrey Ichnowski, Kaiyuan Chen, Karthik Dharmarajan, Simeon Adebola, Michael Danielczuk, Víctor Mayoral-Vilches +9
ICRA 2023poster

Mobility, power, and price points often dictate that robots do not have sufficient computing power on board to run contemporary robot algorithms at desired rates. Cloud computing providers such as AWS, GCP, and Azure offer immense computing power and increasingly low latency on demand, but tapping i…

Cited by 25Source ↗code ↗Scholar ↗