基于熵的理论框架

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港大&清华最新!仅通过少量演示,实现动态物体操作的强泛化能力!
具身智能之心· 2025-08-21 00:03
Group 1 - The article discusses the challenges of dynamic object manipulation in industrial manufacturing and proposes a solution through a new system called GEM (Generalizable Entropy-based Manipulation) that achieves high generalization with minimal demonstration data [3][6]. - GEM combines target-centered geometric perception and mixed action control to effectively reduce data requirements while maintaining high success rates in dynamic environments [6][15]. - The system has been validated in real-world scenarios, achieving a success rate of over 97% in over 10,000 operations without the need for on-site demonstrations [6][44]. Group 2 - Dynamic object manipulation requires higher precision and real-time responsiveness compared to static object manipulation, making it a complex task [8]. - Existing methods face limitations such as the need for extensive demonstration data and poor scalability due to high costs associated with data collection in dynamic environments [11][13]. - The proposed entropy-based framework quantifies the optimization process in imitation learning, aiming to minimize the data needed for effective generalization [13][15]. Group 3 - The GEM system is designed to lower observation entropy and action conditional entropy, which are critical for reducing data requirements [15][16]. - The system utilizes a hardware platform with adjustable-speed conveyor belts and RGB-D cameras to track and manipulate objects effectively [20][21]. - Key components of GEM include a memory encoder that enhances performance by integrating historical data and a mixed action control mechanism that simplifies dynamic challenges [29][39]. Group 4 - Experimental results show that GEM outperforms seven mainstream methods in both simulated and real-world scenarios, with an average success rate of 85% [30][31]. - The system demonstrates robust performance across various moving speeds and object geometries, maintaining high success rates even with unseen objects [38][39]. - In practical applications, GEM has been successfully deployed in a cafeteria setting, handling challenges such as food residue and fast-moving items with a success rate of 97.2% [42][44].