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Manipulation in Human Environments
Aaron Edsinger & Charlie Kemp Humanoid Robotics Group MIT CSAIL
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Domo
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Manipulation in Human Environments
Work with everyday objects Collaborate with people
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Applications Aging in place Cooperative manufacturing Household chores
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Three Themes Use Your Body Social Manipulation Task relevant features
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Use Your Body Simplify perception (tool tip, hand)
Test assumptions (flat surface) Compliance simplifies contact (Placing, Grasping and Transferring)
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Structure In Human Environments
Sense from above Flat surfaces Objects for human hands Objects for use by humans Look the user in the eye Interpretable body Tall and narrow
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Social Complementary action
Person can simplify perception and action for the robot Robot can cue the human intuitively (body language) Lot's of examples of tasks where a robot can be helpful without doing everything (robot doesn't have to solve everything to be helpful)
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Task Relevant Features
What is important? What is irrelevant? *Distinct from object detection/recognition.
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Other Examples Donald Norman Circular openings Tips Handles
Contact Surfaces
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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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Distinct Perceptual Problem
Not object recognition How should it be used Distinct methods and features
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Generalize What You've Learned
Across objects Perceptually map tasks across objects key features -> key features Across manipulators Motor equivalence Manipulator details may be irrelevant
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Tool Tip Detection Visual + motor detection method Kinematic Estimate
Visual Model
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Acquire a Visual Model
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Use The Hand's Frame Combine weak evidence Rigidly grasped
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RSS 2006 Workshop Manipulation for Human Environments Much to be done!
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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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Mean Pixel Error for Automatic and Hand Labeled Tip Detection
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Mean Pixel Error for Hand Labeled, Multi-Scale Detector, and Point Detector
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Learning from Demonstration
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The Detector Responds To
Fast Motion Convex
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Video from Eye Camera Motion Weighted Edge Map Multi-scale Histogram (Medial-Axis, Hough Transform for Circles) 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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