1. Combining Motion Planning and Optimization
for Flexible Robot Manipulation
Jonathan Scholz and Mike Stilman
International Conference on Humanoid Robotics, 2010
COMP 790-099, Presenter: Ravikiran Janardhana
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2. Problem Statement
• Design a system/algorithm to solve general
manipulation tasks in natural human
environments
• Involves uncertain dynamics and
underspecified goals
• Service Manipulation Tasks – House Cleaning
to Collaborative Factory Automation
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3. Service Robots
• Challenges – Unfamiliar Objects and Abstract
Goals
• Learn about objects in addition to planning
interactions
• Accept broad variety of goals
Eg:- Setting a table
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4. Related Work
• Probabilistic Roadmaps, Rapidly Exploring
Random Trees
• Model-free Reinforcement Learning
• Model-based learners i.e., learning from
demonstration
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5. Proposed Solution
• Task space based probabilistic planner
• Combine strengths of model based planning
and reinforcement learning i.e., model-based
planning with optimization
• Reaching an optimal world configuration is
more important than finding the optimal way
to reach it
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6. Flexible Manipulation
• Determining the goal or the optimal
configuration
• Finding the forward models for robot actions
• Planning to use the actions to reach the goal
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7. Service Task: Setting a Table
• Consider a dinner where n guests must be
given n plates and m platters must be placed
at the center of the table
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8. Objective Function Specification
• User can specify the goal as an abstract
optimization metric
• Following are the objectives:-
– The plates should be located far from each other
– The platters should be at the center of the table
– The platters should be aligned parallel to the table
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9. Objective Function Specification
• Define two sets of objects: plates P and
platters Q
• Each object location is parameterized by
position and orientation {x, y, θ}
• Environmental constraints – Table Dimensions
xmin ≤ x ≤ xmax; ymin ≤ y ≤ ymax;
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10. Objective Function - Math
• Maximize Plate distance
• Put Platters at Table Center
• Align Platters with Table
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11. Objective Function - Math
• Overall objective function:
• The weights α, β, γ must be specified with
regard to the relative importance of the
subtasks.
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12. Action Model Learning
• Given state space S and actions A, probability
of outcome of any action in any state is
• Probability distribution obtained by
exploration.
• Compute probability models of displacement,
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13. Motion Primitives
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14. Forward Models
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15. Models Achieved
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16. Learning Forward Models - Demo
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17. Motion Planner (Task Space RRT)
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18. Experiments / Results
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19. Experiments / Results
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20. Experiments / Results - Demo
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21. Experiments / Results
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22. Conclusion / Future Work
• The paper presents a general framework for
handling abstract tasks in object manipulation
using reinforcement learning and model based
planning
• Explore broader tools and domains that
increase the generality of task space planning
by combining planning, learning and
optimization
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23. Comments
• Requires tuning of parameters such as σ2ref and ɛ
which are highly task dependent
• Models can be stored for future use
• Collision detection would be complex if problem
size was increased, RRT might then become
deadlocked and algorithm is reduced to random
search
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24. Q&A
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