Manipulation in Human Environments

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Manipulation in Human Environments

• Aaron Edsinger Charlie Kemp
• Humanoid Robotics Group
• MIT CSAIL

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Domo

• 29 DOF
• 6 DOF Series Elastic Actuator (SEA) arms
• 4 DOF SEA hands
• 2 DOF SEA neck
• Active vision head
• Stereo cameras
• Gyroscope
• Sense joint angle torque
• 15 node Linux cluster

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Manipulation in Human Environments
Human environments are designed to match our
cognitive and physical abilities

• Work with everyday objects
• Collaborate with people
• Perform useful tasks

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Applications

• Aging in place
• Cooperative manufacturing
• Household chores

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Three Themes

• Let the body do the thinking
• Collaborative manipulation
• Task relevant features

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Let the Body do the Thinking

• Design
• Passive compliance
• Force control
• Human morphology

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Let the Body do the Thinking

• Compensatory behaviors
• Reduce uncertainty
• Modulate arm stiffness
• Aid perception (motion, visibility)
• Test assumptions (explore)

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Let the Body Do the Thinking
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Collaborative Manipulation

• Complementary actions
• Person can simplify perception and action for the
  robot
• Robot can provide intuitive cues for the human
• Requires matching to our social interface

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Collaborative Manipulation
Social amplification
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Collaborative Manipulation

• A third arm
• Hold a flashlight
• Fixture a part
• Extend our physical abilities
• Carry groceries
• Open a jar
• Expand our workspace
• Place dishes in a cabinet
• Hand a tool
• Reach a shelf

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Task Relevant Features

• What is important?
• What is irrelevant?

Distinct from object detection/recognition.
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Structure In Human Environments
Donald Norman The Design of Everyday Objects
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Structure In Human Environments
Human environments are constrained to match our
cognitive and physical abilities

• Sense from above
• Flat surfaces
• Objects for human hands
• Objects for use by humans

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Why are tool tips common?

• Single, localized interface to the world
• Physical isolation helps avoid irrelevant contact
• Helps perception
• Helps control

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Tool Tip Detection

• Visual motor detection method
• Kinematic Estimate
• Visual Model

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Mean Pixel Error for Automatic and Hand Labelled
Tip Detection
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Mean Pixel Error for Hand Labeled, Multi-Scale
Detector, and Point Detector
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Model-Free Insertion

• Active tip perception
• Arm stiffness modulation
• Human interaction

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Other Examples

• Circular openings
• Handles
• Contact Surfaces
• Gravity Alignment

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FutureGeneralize What You've Learned

• Across objects
• Perceptually map tasks across objects
• Key features map to key features
• Across manipulators
• Motor equivalence
• Manipulator details may be irrelevant

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RSS 2006 Workshop

• Manipulation for Human Environments

Robotics Science and Systems University of
Pennsylvania , August 19th, 2006
manipulation.csail.mit.edu/rss06
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Summary

• Importance of Task Relevant Features
• Example of the tool tip
• Large set of hand tools
• Robust detection (visual motor)
• Kinematic estimate
• Visual model

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In Progress

• Perform a variety of tasks
• Insertion
• Pouring
• Brushing

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Learning from Demonstration
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The Detector Responds To
Fast Motion
Convex
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Motion Weighted Edge Map
Video from Eye Camera
Local Maxima
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Defining Characteristics

• Geometric
• Isolated
• Distal
• Localized
• Convex
• Cultural/Design
• Far from natural grasp location
• Long distance relative to hand size

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Other Task Relevant Features?
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Detecting the Tip
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Include Scale and Convexity
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Distinct Perceptual Problem

• Not object recognition
• How should it be used
• Distinct methods and features

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Use The Hand's Frame

• Combine weak evidence
• Rigidly grasped

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Acquire a Visual Model