Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

718
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
718
Kinematic Equations: Problem Solving01:15

Kinematic Equations: Problem Solving

12.6K
When analyzing one-dimensional motion with constant acceleration, the problem-solving strategy involves identifying the known quantities and choosing the appropriate kinematic equations to solve for the unknowns. Either one or two kinematic equations are needed to solve for the unknowns, depending on the known and unknown quantities. Generally, the number of equations required is the same as the number of unknown quantities in the given example. Two-body pursuit problems always require two...
12.6K
Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

643
Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
The first step to solving a two-dimensional force system problem is to draw a free-body diagram of the object under consideration. This diagram helps identify all the external forces acting on the object, including their...
643
Free-body Diagrams: Problem Solving01:30

Free-body Diagrams: Problem Solving

912
Free-body diagrams are essential tools for physicists and engineers studying the motion of objects. Free-body diagrams are graphical representations of the object or system under consideration, and they focus solely on the essential forces acting on the object. This tool helps break down complex problems into simpler models that are easier to understand and solve.
For example, consider a block with a mass of 10 kg released on an inclined plane at an angle of 30° to the horizontal, where...
912
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

98
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
98
Method of Joints: Problem Solving II01:30

Method of Joints: Problem Solving II

656
Consider a truss structure with frictionless joints fixed to a wall and roller support. If a force of 150 N is applied to joint A, the forces in each member of the truss can be determined using the method of joints.
656

You might also read

Related Articles

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

Sort by
Same author

Decentralized multi-agent reinforcement learning based on best-response policies.

Frontiers in robotics and AI·2024
Same author

A Concise and Geometrically Exact Planar Beam Model for Arbitrarily Large Elastic Deformation Dynamics.

Frontiers in robotics and AI·2021
Same author

A Hybrid Framework for Understanding and Predicting Human Reaching Motions.

Frontiers in robotics and AI·2021
Same author

Adaptation and Transfer of Robot Motion Policies for Close Proximity Human-Robot Interaction.

Frontiers in robotics and AI·2021
Same author

An Inverse Optimal Control Approach to Explain Human Arm Reaching Control Based on Multiple Internal Models.

Scientific reports·2018
Same author

Global localization of 3D point clouds in building outline maps of urban outdoor environments.

International journal of intelligent robotics and applications·2017

Related Experiment Video

Updated: Aug 23, 2025

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
07:52

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

8.8K

Bayesian optimization with unknown constraints in graphical skill models for compliant manipulation tasks using an

Volker Gabler1, Dirk Wollherr1

  • 1All authors are with the Chair of Automatic Control Engineering, TUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.

Frontiers in Robotics and AI
|October 31, 2022
PubMed
Summary

This study enhances robotic manipulation learning by using factor graphs to estimate task success probability in reinforcement learning (RL). This improves sample efficiency for industrial robots in unknown environments.

Keywords:
Bayesian optimizationcompliant manipulationepisodic reinforcement learningrobot learning and controlsafe learning

More Related Videos

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

9.6K
WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

5.0K

Related Experiment Videos

Last Updated: Aug 23, 2025

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
07:52

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

8.8K
A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

9.6K
WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

5.0K

Area of Science:

  • Robotics
  • Machine Learning
  • Control Theory

Background:

  • Industrial robots often lack compliant control for unknown environments.
  • Existing meta-learning for graphical skill-formalisms requires adaptation.
  • Online adaptation of controller parameters is crucial for unknown environments.

Purpose of the Study:

  • To extend meta-learning for graphical skill-formalisms using a hybrid force-velocity controller.
  • To improve online episodic reinforcement learning (RL) performance.
  • To enhance sample efficiency in early learning stages for robotic manipulation.

Main Methods:

  • Implemented a hybrid force-velocity controller within a graphical skill-formalism for industrial robots.
  • Extended the skill-formalism with factor graphs to estimate task success probability.
  • Utilized constraint Gaussian process (GP) models and acquisition functions for optimized sample acquisition.

Main Results:

  • The proposed method enables faster acquisition of feasible samples compared to state-of-the-art approaches.
  • Achieved a smaller regret value, indicating improved learning performance.
  • Demonstrated enhanced learning efficiency, especially when successful samples are sparse.

Conclusions:

  • The integration of factor graphs for success probability estimation significantly boosts robotic manipulation learning.
  • The approach offers a promising solution for efficient online adaptation in unknown environments.
  • This work paves the way for more capable and adaptable industrial robotic systems.