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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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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Service Robots
• Challenges – Unfamiliar Objects and Abstract Goals
• Learn about objects in addition to planning interactions
• Accept broad variety of goalsEg:- Setting a table
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Related Work
• Probabilistic Roadmaps, Rapidly Exploring Random Trees
• Model-free Reinforcement Learning
• Model-based learners i.e., learning from demonstration
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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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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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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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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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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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Objective Function - Math• Maximize Plate distance
• Put Platters at Table Center
• Align Platters with Table
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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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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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Motion Primitives
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Forward Models
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Models Achieved
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Learning Forward Models - Demo
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Motion Planner (Task Space RRT)
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Experiments / Results
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Experiments / Results
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Experiments / Results - Demo
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Experiments / Results
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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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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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Q&A
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