Yixiao (Jimmy) Fang

Yixiao (Jimmy) Fang

Robotics & ML

Publications

Conference Papers

Towards Real-Time Generation of Delay-Compensated Video Feeds for Outdoor Mobile Robot Teleoperation

Published in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2025

Teleoperation is an important technology to enable supervisors to control agricultural robots remotely. However, environmental factors in dense crop rows and limitations in network infrastructure hinder the reliability of data streamed to teleoperators. These issues result in delayed and variable frame rate video feeds that often deviate significantly from the robot’s actual viewpoint. We propose a modular learning-based vision pipeline to generate delay-compensated images in real-time for supervisors. Our extensive offline evaluations demonstrate that our method generates more accurate images compared to state-of-the-art approaches in our setting. Additionally, we are one of the few works to evaluate a delay-compensation method in outdoor field environments with complex terrain on data from a real robot in real-time. Additional videos are provided at project website.

Recommended citation: (under review) N. Chakraborty*, Y. Fang*, et al. "Towards Real-Time Generation of Delay-Compensated Video Feeds for Outdoor Mobile Robot Teleoperation" Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2025
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Symposiums

Learning to Turn: Diffusion Imitation for Robust Row Turning in Under-Canopy Robots

Published in ICRA 40: Special Session on Autonomous Navigation, IEEE International Conference on Robotics and Automation, 2024

Under-canopy agricultural robots require robust navigation capabilities to enable full autonomy but struggle with tight row turning between crop rows due to degraded GPS reception, visual aliasing, occlusion, and complex vehicle dynamics. We propose an imitation learning approach using diffusion policies to learn row turning behaviors from demonstrations provided by human operators or privileged controllers. Simulation experiments in a corn field environment show potential in learning this task with only visual observations and velocity states. However, challenges remain in maintaining control within rows and handling varied initial conditions, highlighting areas for future improvement.

Recommended citation: A. Sivakumar, P. Thangeda, Y. Fang, et al. "Learning to Turn: Diffusion Imitation for Robust Row Turning in Under-Canopy Robots" ICRA 40: Special Session on Autonomous Navigation, IEEE International Conference on Robotics and Automation (ICRA), 2024
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