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Seungyeon Yoo
Seoul National University
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Youngseok Jang
Seoul National University
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Dabin Kim
Seoul National University
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References (35)
[3]
Performance-Guided Refinement for Visual Aerial Navigation using Editable Gaussian Splatting in FalconGym 2.0
Founder's Pitch
"ReaDy-Go offers a dynamic 3D simulation platform for robust environment-specific visual navigation in robots, closing the sim-to-real gap."
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4/4 signals
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Why It Matters
The ReaDy-Go pipeline addresses the challenging problem of sim-to-real transfer in robotics by providing a high-fidelity simulation environment for dynamic and site-specific scenarios. This is crucial as it enhances autonomous systems' ability to operate effectively in varied and unpredictable real-world environments, such as homes and factories, improving safety and navigation performance.
Product Angle
The product could be packaged as a simulation service platform targeting robotics and autonomous vehicle industries. By offering customizable, photorealistic simulation scenarios with dynamic elements, it would aid in training and testing navigation models without the need for extensive real-world data collection.
Disruption
ReaDy-Go has the potential to disrupt current methods of robotic navigation training by removing reliance on static datasets and expensive real-world testing. It offers a scalable solution for generating environment-specific datasets that bridge the simulation-to-reality gap.
Product Opportunity
The robotics simulation market is growing rapidly, particularly due to increased interest in autonomous systems. Companies deploying robots in dynamic environments like warehouses or urban streets could benefit from this technology, reducing costs of physical prototyping and testing.
Use Case Idea
A commercial application could be in robotics companies developing autonomous navigation systems for homes and factories, using ReaDy-Go to build dynamic training environments that help improve their robots' navigation capabilities through robust simulation datasets.
Science
ReaDy-Go employs a novel approach using 3D Gaussian Splatting (GS) to create realistic simulations of dynamic environments. The pipeline involves reconstructing a static scene and integrating dynamic human-like obstacles to train visual navigation models. This approach allows for realistic simulations that account for the sim-to-real gap by capturing high-fidelity visuals and physics of the environment, supported by human animation modules to introduce dynamic elements realistically.
Method & Eval
The ReaDy-Go method involves generating dynamic environment datasets, training visual navigation policies using these datasets, and evaluating the trained policies both in simulated environments and real-world scenarios. It outperformed traditional systems across multiple tests, illustrating better navigation and adaptation to dynamic obstacles.
Caveats
One limitation could be the scalability of the dynamic human GS animations in highly complex or densely populated scenes. Also, reliance on monocular vision and 3D Gaussian Splatting may introduce inaccuracies compared to more sophisticated multi-modal systems.
Author Intelligence
H. Jin Kim
LEAD Seoul National University
hjinkim@snu.ac.kr
Seungyeon Yoo
Seoul National University
Youngseok Jang
Seoul National University
Dabin Kim
Seoul National University
Youngsoo Han
Seoul National University
Seungwoo Jung
Seoul National University