Gamma-Art: Bridging Simulated and Real-World Articulated Object Manipulation
伽马智能 & TeleAI：Gamma-Art 让铰接物体操作从仿真走向现实
Gamma-Art is a synthetic data synthesis framework designed to train robots to manipulate articulated objects such as doors, drawers, and knobs. It uses simulation to generate diverse, randomized training data, which is then used to train models that can generalize to real-world scenarios. The system includes a simulation platform (Gamma-Sim) that generates and validates trajectories, and a dataset (Gamma-Art-Data) containing over 1 million trajectories. The approach enables robots to perform complex tasks in real-world environments.
Gamma-Art introduces a synthetic data synthesis framework for articulated object manipulation, enabling the training of robotic systems to handle complex interactions with hinges, sliders, and rotating parts. The system leverages simulation to generate diverse, randomized training data, which is then used to train models that can generalize to real-world scenarios.
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