用持续学习解决软指传感器信号漂移问题，提升长期感知稳定性。
Adaptive Drift Compensation for Soft Sensorized Finger Using Continual Learning
- 结合LSTM与记忆回放，动态补偿传感器信号漂移。
- 在9次重置实验中，模型保持高精度，显著优于对比方法。
- 适合长期运行的柔性机器人触觉系统，如抓取任务。
应变传感器因其柔韧性和易集成性，在软体机器人触觉感知中日益流行。触觉感知对软夹持器至关重要，使其能在非结构化环境中安全交互并精确检测物体属性。然而，这类系统面临高非线性、时变行为和长期信号漂移等挑战。本文提出一种持续学习（CL）方法，用于建模基于压电应变传感器的软指触觉感知。为应对上述问题，设计了一种自适应持续学习算法，融合长短期记忆网络（LSTM）与记忆缓冲区进行回放，并引入正则化项使模型决策边界贴近基线信号，同时适应时变漂移。我们进行了九组不同实验，每次均重置整个系统以展示信号漂移现象。还与两种其他方法进行对比，并开展消融实验，评估各组件对整体性能的影响。
原文摘要 · Abstract (English)
Strain sensors are gaining popularity in soft robotics for acquiring tactile data due to their flexibility and ease of integration. Tactile sensing plays a critical role in soft grippers, enabling them to safely interact with unstructured environments and precisely detect object properties. However, a significant challenge with these systems is their high non-linearity, time-varying behavior, and long-term signal drift. In this paper, we introduce a continual learning (CL) approach to model a soft finger equipped with piezoelectric-based strain sensors for proprioception. To tackle the aforementioned challenges, we propose an adaptive CL algorithm that integrates a Long Short-Term Memory (LSTM) network with a memory buffer for rehearsal and includes a regularization term to keep the model's decision boundary close to the base signal while adapting to time-varying drift. We conduct nine different experiments, resetting the entire setup each time to demonstrate signal drift. We also benchmark our algorithm against two other methods and conduct an ablation study to assess the impact of different components on the overall performance.
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