Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...

Force Classification

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Multimodal information structuring with single-layer soft skins and high-density electrical impedance tomography.

Science robotics·2025

Same author

Visuo-dynamic self-modelling of soft robotic systems.

Frontiers in robotics and AI·2024

Same author

Sensorized Skin With Biomimetic Tactility Features Based on Artificial Cross-Talk of Bimodal Resistive Sensory Inputs.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2023

Same author

Autonomous detection and sorting of litter using deep learning and soft robotic grippers.

Frontiers in robotics and AI·2022

Same author

3D Printable Soft Sensory Fiber Networks for Robust and Complex Tactile Sensing.

Micromachines·2022

Same author

Touchless interactive teaching of soft robots through flexible bimodal sensory interfaces.

Nature communications·2022

Same journal

Editorial: Robotic applications for a sustainable future.

Frontiers in robotics and AI·2026

Same journal

Passive wheels on legged robots: a survey.

Frontiers in robotics and AI·2026

Same journal

Politeness cannot make up for robots' errors.

Frontiers in robotics and AI·2026

Same journal

Workers expect basic social skills but limited autonomy from future robots - a qualitative interview study and taxonomy for robot social skills.

Frontiers in robotics and AI·2026

Same journal

Human-robot interaction in sustainable hospitality: how robot type shapes customer emotions, green perceptions, and service loyalty.

Frontiers in robotics and AI·2026

Same journal

Dynamic variance-aware federated tuning for efficient autonomous vehicle perception under non-IID settings.

Frontiers in robotics and AI·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 10, 2025

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

Bridging vision and touch: advancing robotic interaction prediction with self-supervised multimodal learning.

Luchen Li¹, Thomas George Thuruthel¹

¹Department of Computer Science, University College London, London, United Kingdom.

Frontiers in Robotics and AI

|October 15, 2024

Summary

This summary is machine-generated.

Robotic learning benefits from combining vision and tactile data for predicting environmental changes. This multi-modal approach enhances robot understanding and control in complex interactions.

Keywords:

information fusion and compression multi-modal sensing physical robotic interaction predictive learning self-supervised learning

More Related Videos

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Related Experiment Videos

Last Updated: Jun 10, 2025

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

Robotics
Artificial Intelligence
Machine Learning

Background:

Predicting environmental consequences of robotic actions is crucial for advanced AI.
Current robotic learning often relies solely on vision and motion data.
Complex tasks require richer sensory perception beyond vision.

Purpose of the Study:

Investigate the interdependence between vision and tactile sensation in dynamic robotic interactions.
Develop a multi-modal fusion mechanism for action-conditioned video prediction.
Enhance robotic control through integrated sensory data.

Main Methods:

Introduced a multi-modal fusion mechanism to action-conditioned video prediction models.
Developed a robotic interaction system with cameras and vision-based tactile sensors.
Collected vision-tactile sequences and robot action data for training and evaluation.

Main Results:

Demonstrated that fusing vision and tactile data improves video prediction accuracy.
Revealed an asymmetrical impact of different sensory modalities on environmental interpretation.
Showcased the effectiveness of the multi-modal fusion in complex robotic tasks.

Conclusions:

Multi-modal sensory fusion, particularly vision and tactile data, significantly advances robotic learning.
Understanding cross-modality influences enables more adaptive and efficient robotic control.
This research paves the way for enhanced dexterous manipulation and human-robot interaction.