COMP 775 Motion planning paper presentation

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  • Introduction of the paper and the authors
  • Briefly explain meaning of uncertain dynamics and underspecified goals.
  • Stress on the need for learning about objects in addition to planning. Give some more examples for broad variety of goals.
  • Explain the caution w.r.t PRM and RRTs. Explain reinforcement learning and talk about the limitations of the related work.
  • Setup the next slide so that listeners can understand what is task space. Explain as to what do you mean by strengths of reinforcement learning and model based planning. Define optimal world configuration.
  • Explain forward models briefly
  • Explain table setting problem
  • Explain the equations, (xg,yg) and the last equation in detail
  • Touch on Markov Decision Process and on how probability distribution and probability models of displacement is computed.
  • Explain the bounding box and displacement of 5cm vector displacement.
  • It is RRT effectively with 2 modifications namely:- a) Select state action pair which results in node closest to a sample point b) Direct GD heuristic to reach global minimum faster. Explain significance of “epsilon” and distance metric “rho”
  • SRLib block and cylinder primitives used.
  • Talk about the general framework presented and how it can be used to solve a variety of manipulation tasks
  • Talk about empirically determined (determined by experiments or observation) parameters and how models can be re-used and shared.
  • COMP 775 Motion planning paper presentation

    1. 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 1
    2. 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 2
    3. 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 3
    4. 4. Related Work• Probabilistic Roadmaps, Rapidly Exploring Random Trees• Model-free Reinforcement Learning• Model-based learners i.e., learning from demonstration 4
    5. 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 5
    6. 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 6
    7. 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 7
    8. 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 8
    9. 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; 9
    10. 10. Objective Function - Math• Maximize Plate distance• Put Platters at Table Center• Align Platters with Table 10
    11. 11. Objective Function - Math• Overall objective function:• The weights α, β, γ must be specified with regard to the relative importance of the subtasks. 11
    12. 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, 12
    13. 13. Motion Primitives 13
    14. 14. Forward Models 14
    15. 15. Models Achieved 15
    16. 16. Learning Forward Models - Demo 16
    17. 17. Motion Planner (Task Space RRT) 17
    18. 18. Experiments / Results 18
    19. 19. Experiments / Results 19
    20. 20. Experiments / Results - Demo 20
    21. 21. Experiments / Results 21
    22. 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 22
    23. 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 23
    24. 24. Q&A 24

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