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Related Experiment Video

Updated: Nov 4, 2025

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

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

Kaiyu Hang¹, Walter G Bircher², Andrew S Morgan²

¹Department of Mechanical Engineering and Material Science, Yale University, New Haven, CT, USA. kaiyu.hang@yale.edu.

Science Robotics

|May 27, 2021

Summary

This summary is machine-generated.

Robots can now learn object manipulation skills by themselves using minimal sensors. This new method, virtual linkage-based representations (VLRs), enables precise robotic in-hand manipulation without prior knowledge.

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Area of Science:

Robotics
Control Theory
Machine Learning

Background:

Precise robotic in-hand manipulation requires accurate models of hand-object interactions.
Existing methods often rely on pre-known parameters or extensive sensing, limiting adaptability.

Purpose of the Study:

To develop a method for robots to autonomously identify hand-object model parameters with minimal sensing.
To enable precise in-hand manipulation through self-identified models.

Main Methods:

Derivation of algorithms based on virtual linkage-based representations (VLRs).
Utilizing exploratory manipulation and probabilistic reasoning for self-identification.
Implementation on a Yale Model O hand using only an in-hand camera.

Main Results:

Successful self-identification of VLRs, even with novel finger designs.
Demonstrated effective control for precise in-hand manipulation tasks.
Passive adaptability of the underactuated hand facilitated the self-identification process.

Conclusions:

The proposed VLR framework enables robots to self-identify manipulation mechanics.
This approach facilitates precise in-hand manipulation control using minimal sensing.
The system shows promise for adaptable and robust robotic manipulation.