Manipulation for self-Identification, and self-Identification for better manipulation

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.

Automated summary

The study introduces an algorithm using virtual linkage-based representations (VLRs) to self-identify the underlying mechanics of hand-object systems through exploratory manipulation actions and probabilistic reasoning, enabling precise robotic in-hand manipulation control. The framework, validated on a Yale Model O hand without joint encoders or tactile sensors, demonstrates effectiveness even when some fingers are replaced with novel designs. The VLR is shown to be effective in applications such as handwriting, marble maze playing, and cup stacking, highlighting its potential for real-world robotic manipulation tasks.