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Related Experiment Video

Updated: Dec 4, 2025

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace

Published on: August 8, 2019

Human control of complex objects: Towards more dexterous robots.

Salah Bazzi^1,2, Dagmar Sternad^1,2,3

¹Department of Biology, Northeastern University, Boston, Massachusetts 02115, USA.

Advanced Robotics : the International Journal of the Robotics Society of Japan

|October 26, 2020

Summary

This summary is machine-generated.

Humans learn to control complex object dynamics by making interactions predictable, not by maximizing smoothness. This insight can improve robot control strategies for tasks like handling a coffee cup.

Keywords:

chaos complex object manipulation contraction human motor control stability underactuated

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Last Updated: Dec 4, 2025

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace

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Investigating Motor Skill Learning Processes with a Robotic Manipulandum

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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

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Published on: April 21, 2023

Area of Science:

Robotics
Human-Computer Interaction
Control Theory

Background:

Robotic manipulation of objects with underactuated dynamics is challenging.
Humans effectively use tools and complex object dynamics, offering insights for robotic control.

Purpose of the Study:

To investigate human strategies for controlling objects with complex, nonlinear, and unpredictable dynamics.
To understand how humans make these interactions predictable for robotic applications.

Main Methods:

Subjects interacted with virtual objects (cup, ball) in a virtual environment using a robotic visual and haptic interface.
Quantified predictability using control contraction metrics and mutual information between controller and object.

Main Results:

In point-to-point tasks, subjects utilized object dynamics' contracting regions to manage perturbations.
Human controllers demonstrated exponential trajectory stabilization.
For continuous tasks, subjects prioritized predictability over smoothness and energy efficiency.

Conclusions:

Humans develop predictable control strategies for complex object dynamics, rather than relying on simple smoothness.
Findings can inform the development of dexterous robotic manipulators and enhance human-robot interaction.