Manipulation for self-Identification, and self-Identification for better manipulation
Manipulation for self-Identification, and self-Identification for better manipulation
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DOI：
10.1126/scirobotics.abe1321
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发表时间：
2021-05-26
期刊：
影响因子：
25
通讯作者：
Dollar, Aaron M.
中科院分区：
文献类型：
--
作者：
Hang, Kaiyu;Bircher, Walter G.;Dollar, Aaron M.
手-物体一系列参数的建模过程对于机器人精确可控的手操作至关重要，因为它可以获得手的驱动输入到物体运动的映射。在不假设这些模型参数中的大多数是先验已知的或可以很容易地由传感器估计的情况下，我们专注于为机器人配备使用最小传感主动自我识别必要模型参数的能力。在这里，我们基于虚拟链接表示（VLRs）的概念推导出算法，通过探索性操作动作和概率推理来自我识别手-物体系统的潜在机制，反过来，表明自我识别的VLR可以实现精确的手-物体操作控制。为了验证我们的框架，我们在没有关节编码器或触觉传感器的Yale Model O手上实例化了所提出的系统。欠驱动手的被动适应性极大地促进了自我识别过程，因为它们在随机探索过程中自然地确保了稳定的手-物交互。仅依靠手持相机，我们的系统可以有效地自我识别vlr，即使一些手指被新的设计取代。此外，我们还展示了手写，大理石迷宫游戏和杯子堆叠的手操作应用，以证明VLR在精确的手操作控制中的有效性。
The process of modeling a series of hand-object parameters is crucial for precise and controllable robotic in-hand manipulation because it enables the mapping from the hand's actuation input to the object's motion to be obtained. Without assuming that most of these model parameters are known a priori or can be easily estimated by sensors, we focus on equipping robots with the ability to actively self-identify necessary model parameters using minimal sensing. Here, we derive algorithms, on the basis of the concept of virtual linkage-based representations (VLRs), to self-identify the underlying mechanics of hand-object systems via exploratory manipulation actions and probabilistic reasoning and, in turn, show that the self-identified VLR can enable the control of precise in-hand manipulation. To validate our framework, we instantiated the proposed system on a Yale Model O hand without joint encoders or tactile sensors. The passive adaptability of the underactuated hand greatly facilitates the self-identification process, because they naturally secure stable hand-object interactions during random exploration. Relying solely on an in-hand camera, our system can effectively self-identify the VLRs, even when some fingers are replaced with novel designs. In addition, we show in-hand manipulation applications of handwriting, marble maze playing, and cup stacking to demonstrate the effectiveness of the VLR in precise in-hand manipulation control.