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Related Concept Videos

Purposive Learning01:22

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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Learning latent actions to control assistive robots.

Dylan P Losey1, Hong Jun Jeon2, Mengxi Li2

  • 1Mechanical Engineering Department, Virginia Tech, Blacksburg, USA.

Autonomous Robots
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Summary
This summary is machine-generated.

This study introduces latent actions to intuitively control high-dimensional assistive robot arms using low-dimensional interfaces like joysticks. This approach enhances user control for people with disabilities performing daily tasks.

Keywords:
Assistive roboticsLatent representationsShared autonomyTeleoperation

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Area of Science:

  • Robotics
  • Human-Computer Interaction
  • Assistive Technology

Background:

  • Assistive robot arms offer dexterity but are controlled by limited low-dimensional interfaces.
  • Current control methods use pre-defined mappings that do not align with user tasks.
  • This mismatch hinders intuitive operation for everyday activities.

Purpose of the Study:

  • To develop an intuitive control system for high-dimensional assistive robot arms.
  • To enable users to control robots through low-dimensional interfaces by embedding high-dimensional actions into latent actions.
  • To personalize the control mapping between user inputs and robot actions.

Main Methods:

  • Learning latent actions from offline task demonstrations.
  • Combining learned latent actions with autonomous robot assistance for goal achievement.
  • Developing a personalized alignment model between joystick inputs and latent actions.

Main Results:

  • Successfully embedded high-dimensional robot actions into low-dimensional latent actions.
  • Demonstrated effective user control in diverse tasks (e.g., cooking, assembly).
  • Validated the approach with both non-disabled and disabled users.

Conclusions:

  • Latent actions provide an intuitive and user-friendly method for controlling assistive robots.
  • Personalized alignment models significantly improve the usability of assistive robotic systems.
  • This research advances the capability of assistive robots for individuals with disabilities.