manipulation in human environments

Edsinger+Kemp NEMS 2006

Manipulation in Human Environments

Aaron Edsinger & Charlie KempHumanoid Robotics Group

MIT CSAIL


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


Manipulation in Human Environments

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

Human environments are designed to match our cognitive and physical abilities


Applications

● Aging in place

● Cooperative manufacturing

● Household chores


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)


Let the Body Do the Thinking




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


Collaborative ManipulationSocial amplification


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


Task Relevant Features

● What is important?● What is irrelevant?

*Distinct from object detection/recognition.


Structure In Human Environments

Donald NormanThe Design of Everyday Objects


Structure In Human Environments

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

Human environments are constrained to match our cognitive and physical abilities


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


Mean Pixel Error for Automatic and Hand Labelled Tip Detection


Mean Pixel Error for Hand Labeled, Multi-Scale Detector, and Point Detector


Model-Free Insertion



•Active tip perception

•Arm stiffness modulation

•Human interaction


Other Examples

● Circular openings● Handles● Contact Surfaces● Gravity Alignment


Future:Generalize 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


RSS 2006 WorkshopManipulation for Human Environments

Robotics: Science and Systems University of Pennsylvania , August 19th, 2006

manipulation.csail.mit.edu/rss06


Summary

● Importance of Task Relevant Features

● Example of the tool tip– Large set of hand tools– Robust detection (visual + motor)– Kinematic estimate– Visual model


In Progress

● Perform a variety of tasks– Insertion– Pouring– Brushing


Learning from Demonstration


The Detector Responds To

Fast Motion Convex


Multi-scale Histogram (Medial-Axis, Hough Transform for Circles)

Motion Weighted Edge MapVideo from Eye Camera

Local Maxima


Defining Characteristics● Geometric

– Isolated– Distal– Localized– Convex

● Cultural/Design– Far from natural grasp location– Long distance relative to hand size


Other Task Relevant Features?


Detecting the Tip


Include Scale and Convexity


Distinct Perceptual Problem

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


Use The Hand's Frame

● Combine weak evidence● Rigidly grasped


Acquire a Visual Model

manipulation in human environments

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