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Learning From Demonstration Atkeson and Schaal. Dang, RLAB Feb 28 th , 2007. Goal. Robot Learning from Demonstration Small number of human demonstrations Task level learning (learn intent, not just mimicry) Explore Parametric vs. nonparametric learning role of a priori knowledge.

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Presentation Transcript
slide2
Goal
  • Robot Learning from Demonstration
    • Small number of human demonstrations
    • Task level learning (learn intent, not just mimicry)
  • Explore
    • Parametric vs. nonparametric learning
    • role of a priori knowledge

Dang, RLAB

known task
Known Task
  • Pendulum swing-up task
    • Like pole balancing, but more complex
    • Difficult, but easy to evaluate success
  • Simplified
    • Restricted to horz. motion
    • Impt. variables picked out
      • Pendulum angle
      • Pendulum angular velocity
      • Hand location
      • Hand velocity
      • Hand acceleration

Dang, RLAB

implementation details
Implementation details
  • SARCOS 7DOF arm
  • Stereo Vision, colored ball indicators
  • 0.12s delay overcome with Kalman filter
    • Idealized pendulum dynamics
  • Redundant inverse kinematics and real-time inverse dynamics for control

Dang, RLAB

learning
Learning
  • Task composed of two subtasks
      • Believe that subtask learning accelerates new task learning
    • 1 Pole Swing up
      • open-loop
    • 2 Upright Balance
      • Feedback
  • Focus here on swing-up
    • Balancing already learned

Dang, RLAB

first approach
First approach
  • Directly mimic human hand movement
    • Fails
      • Differences in human and robot capabilities
      • Improper demonstration (not horizontal)
      • Imprecise mimicry

Dang, RLAB

approach the second
Approach the second
  • Learn reward
  • Learn a model
  • Use human demonstration as seed so a planner can find a good policy

Dang, RLAB

learn task model
Learn Task Model
  • Parametric:
    • learn parameters via linear regression
  • Nonparametric
    • Use Locally Weighted Learning
    • Given desired variable and a set of possibly relevant input variables
      • Cross validation to tune meta-parameters

Dang, RLAB

swing up
Swing up
  • Transition to balance occurs at ± 0.5 radians with angular vel. < 3 rad/sec
  • Reward function set to make robot want to be like demonstrator

Dang, RLAB

parametric
Parametric
  • Parameters learned from failure data
  • Trajectory optimized using human trajectory as seed
  • SUCCESS

Dang, RLAB

nonparametric
Nonparametric
  • Slower, but still successful

Dang, RLAB

harder task
Harder Task
  • Double pump swing up
    • Approach fails
      • Believed to be due to improper modeling of the system
      • Solved by

Dang, RLAB

direct task level learning
Direct task-level learning
  • Learn a correction term to add to the target angle
    • Now target ± (0.5+∆)rad
    • Use binary search
  • Worked for parametric
  • Didn’t for nonparametric
    • Left region of validity of local models
    • So, tweak velocity all over
      • Binary search for coefficient

Dang, RLAB

results
Results

Dang, RLAB

summary of technique
Summary of Technique

Succeeds for

Math

Watch demo, mimic hand

None

Learn model,

optimize demo trajectory

Parametric, single

Tune model, reoptimize

Nonparametric, single

Binary search for delta

Parametric, double

Binary search for c

Nonparametric, double

Dang, RLAB

discussion points
Discussion points
  • Reward function was given or learned?
  • Does task-level direct learning make sense?
    • Only useful in this task / implementation?
    • I in PID?
  • Nonparametrics don’t avoid all modeling errors
    • Poor planner?
    • Not enough data?
  • A priori knowledge
    • human selects inputs, outputs, control system, perception, model selection, reward function, task segmenting, task factors
  • It Works!

Dang, RLAB

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