Joris De Schutter

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KU Leuven

Belgium

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ERC Advanced Grant ROBOTGENSKILL

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Articles (9)

A generalization of the Riccati recursion for equality‐constrained linear quadratic optimal control

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Year:

2024

Invariant Descriptors of Motion and Force Trajectories for Interpreting Object Manipulation Tasks in Contact

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Year:

2023

Effect of the soft tissue artifact on marker measurements and on the calculation of the helical axis of the knee during a squat movement: A study on the CAMS-Knee dataset.

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Year:

2022

Autonomous Runtime Composition of Sensor-Based Skills Using Concurrent Task Planning

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Year:

2021

Force from Shape—Estimating the Location and Magnitude of the External Force on Flexible Instruments

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Year:

2021

Reconfigurable Constraint-Based Reactive Framework for Assistive Robotics With Adaptable Levels of Autonomy

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Year:

2021

Shape-Preserving and Reactive Adaptation of Robot End-Effector Trajectories

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Year:

2021

Virtual Motor Torque Sensing for Multirotor Propulsion Systems

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Year:

2021

A Framework for Recognition and Prediction of Human Motions in Human-Robot Collaboration Using Probabilistic Motion Models

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Year:

2020

Collaborators (1)

Andrea Ancillao

TU Wien

AUSTRIA

Joris De Schutter

Research Interests

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Recent Grants

Articles (9)

A generalization of the Riccati recursion for equality‐constrained linear quadratic optimal control

Invariant Descriptors of Motion and Force Trajectories for Interpreting Object Manipulation Tasks in Contact

Effect of the soft tissue artifact on marker measurements and on the calculation of the helical axis of the knee during a squat movement: A study on the CAMS-Knee dataset.

Autonomous Runtime Composition of Sensor-Based Skills Using Concurrent Task Planning

Force from Shape—Estimating the Location and Magnitude of the External Force on Flexible Instruments

Reconfigurable Constraint-Based Reactive Framework for Assistive Robotics With Adaptable Levels of Autonomy

Shape-Preserving and Reactive Adaptation of Robot End-Effector Trajectories

Virtual Motor Torque Sensing for Multirotor Propulsion Systems

A Framework for Recognition and Prediction of Human Motions in Human-Robot Collaboration Using Probabilistic Motion Models

Collaborators (1)

Andrea Ancillao

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