![Dynamic system ,[object Object],[object Object],[object Object],x 1 x 2 x 3 y 1 y 2 y 3](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Similaire à November 30, Projects
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November 30, Projects
- 1. Particle Filters A Collaborative Explanation By: Neeti Wagle & Mikael Pryor
- 8. Examples Sample space Posterior density Miodrag Bolic, Lecture for the School of Information Technology and Engineering at the University of Ottawa, mbolic@site.uottawa.ca Haris Baltzakis, November 2004, Kalman/Particle Filters Tutorial
- 9. Examples Source : Robotics and State Estimation Lab, University of Washington
- 10. Two Robots Play “Catch” By: Mikael Pryor [email_address]
- 11. Pong
- 19. The X-Noise Final Product
- 20. OBJECT LOCALIZATION USING A PHYSICS SIMULATOR Neeti Wagle University of Colorado at Boulder
- 26. PHYSICS SIMULATOR PHYSICS SIMULATOR OBJECT GEOMETRIES PARTICLE FILTERS
- 31. EXAMPLE 1
- 32. EXAMPLE 1
- 33. EXAMPLE 1
- 34. EXAMPLE 2
- 35. EXAMPLE 2
- 36. EXAMPLE 2
- 37. EXAMPLE 3
- 38. EXAMPLE 3
- 39. EXAMPLE 3
- 45. QUESTIONS?
Notes de l'éditeur
- Goal : is to reason the way humans do But this is what the robot really sees This plot is showing the particle filters for a scene that the robot is sensing
- And by that I mean what if the objects could interact with the robot and be agents in their own right? Consider : This comm can either be passive or active. In passive…, In active the objects can actively make service request to the robot. A longterm motivating example is the dishwasher asking the robot to unload it. So these 2 factors really change the way we look at this problem. ….
- And by that I mean what if the objects could interact with the robot and be agents in their own right? Consider : This comm can either be passive or active. In passive…, In active the objects can actively make service request to the robot. A longterm motivating example is the dishwasher asking the robot to unload it. So these 2 factors really change the way we look at this problem. ….
- So the robot has the physics simulator which can serve as its conceptual knowledge of the world. It also has information about the geometries of the obj it senses. Finally, it continues to estimate the object locations using PFs. If you look at this model, we see that it is very similar to the way humans reason about their env and come to conclusions. But we are not quite sure how they use the conceptual knowledge to do that. So even with this powerful tool, the question still remains “what do we query the physics simulator about?”