Learning a State Transition Model of an Underactuated Adaptive Hand
Learning a State Transition Model of an Underactuated Adaptive Hand
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DOI：
10.1109/lra.2019.2894875
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发表时间：
2019-01
影响因子：
5.2
通讯作者：
A. Sintov;A. S. Morgan;A. Kimmel;A. Dollar;Kostas E. Bekris;Abdeslam Boularias
中科院分区：
文献类型：
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作者：
A. Sintov;A. S. Morgan;A. Kimmel;A. Dollar;Kostas E. Bekris;Abdeslam Boularias
完全激活的机器人手通常是昂贵的，而低成本的人出现了，但由于缺乏分析模型，挑战旨在从最低限度的用户工作中自动学习此类型号正在确定给定准静态运动的表达手状态所需的主要，明智的特征，从而可以从记录的轨迹中学习概率的过渡模型。评估局部GP回归的度量是通过称为扩散图的多种学习方法，以发现数据所在的较低维度，并提供了地理位置指标。在对象位置，执行角度和执行器载荷中，充分表达了手动对象系统的配置，并可以为相对长的地平线提供足够的预测。承诺达到这种可预测性的人。在操作过程中，应避免使用该模型的实用性，以将其与闭环控制融合到成功，安全地完成操作任务。
Fully actuated multifingered robotic hands are often expensive and fragile. Low-cost underactuated hands are appealing but present challenges due to the lack of analytical models. This letter aims to learn a stochastic version of such models automatically from data with minimum user effort. The focus is on identifying the dominant, sensible features required to express hand state transitions given quasi-static motions, thereby enabling the learning of a probabilistic transition model from recorded trajectories. Experiments both with Gaussian processes (GP) and neural etwork models are included for analysis and evaluation. The metric for local GP regression is obtained with a manifold learning approach, known as Diffusion Maps, to uncover the lower-dimensional subspace in which the data lies and provide a geodesic metric. Results show that using Diffusion Maps with a feature space composed of the object position, actuator angles, and actuator loads, sufficiently expresses the hand-object system configuration and can provide accurate enough predictions for a relatively long horizon. To the best of the authors’ knowledge, this is the first learned transition model for such underactuated hands that achieves this level of predictability. Notably, the same feature space implicitly embeds the size of the manipulated object and can generalize to new objects of varying sizes. Furthermore, the learned model can identify states that are on the verge of failure and which should be avoided during manipulation. The usefulness of the model is also demonstrated by integrating it with closed-loop control to successfully and safely complete manipulation tasks.