融合触觉反馈的机器人系统提升卵母细胞操作成功率
Haptic-Informed ACT with a Soft Gripper and Recovery-Informed Training for Pseudo Oocyte Manipulation
- 用触觉信息增强动作分块模型，实现实时抓握失败检测
- 在动态环境下任务成功率显著提升，优于传统视觉主导方法
- 适合需要精细操作的生物医学自动化场景
本文提出一种融合触觉信息的ACT机器人系统，用于模拟卵母细胞操作。传统卵母细胞转移依赖视觉感知，常因生物差异和环境干扰需人工干预。新系统引入触觉反馈，结合基于Transformer的动作分块机制，实现抓握失败的实时检测与自适应修正。同时采用3D打印的TPU软夹爪，支持精细操作。实验表明，在动态环境中该系统显著提升了任务成功率、鲁棒性和适应性，验证了多模态学习在生物医学机器人自动化中的潜力。
原文摘要 · Abstract (English)
In this paper, we introduce Haptic-Informed ACT, an advanced robotic system for pseudo oocyte manipulation, integrating multimodal information and Action Chunking with Transformers (ACT). Traditional automation methods for oocyte transfer rely heavily on visual perception, often requiring human supervision due to biological variability and environmental disturbances. Haptic-Informed ACT enhances ACT by incorporating haptic feedback, enabling real-time grasp failure detection and adaptive correction. Additionally, we introduce a 3D-printed TPU soft gripper to facilitate delicate manipulations. Experimental results demonstrate that Haptic-Informed ACT improves the task success rate, robustness, and adaptability compared to conventional ACT, particularly in dynamic environments. These findings highlight the potential of multimodal learning in robotics for biomedical automation.
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