1. L ABORATORY FOR P ERCEPTUAL R OBOTICS U NIVERSITY OF M ASSACHUSETTS A MHERST D EPARTMENT OF C OMPUTER S CIENCE A Relational Representation for Procedural Task Knowledge Stephen Hart Roderic Grupen David Jensen Laboratory for Perceptual Robotics University of Massachusetts Amherst New England Manipulation Symposium May 25, 2005

2. L ABORATORY FOR P ERCEPTUAL R OBOTICS U NIVERSITY OF M ASSACHUSETTS A MHERST D EPARTMENT OF C OMPUTER S CIENCE Introduction and Motivation Robots performing tasks in real-world environments require methods to: Produce fault-tolerant behavior Focus on most salient and relevant information Handle multi-modal, continuous data Leverage past experience (i.e. adapt and reuse) Can we learn probability estimates regarding the effects of sensorimotor variables on task success? –e.g. If I take these actions, how likely am I to succeed at my task?

3. L ABORATORY FOR P ERCEPTUAL R OBOTICS U NIVERSITY OF M ASSACHUSETTS A MHERST D EPARTMENT OF C OMPUTER S CIENCE Generalized Task Expertise Declarative knowledge –Captures abstract knowledge about the task –e.g. find an object, reach to it, pick it up... Procedural knowledge –Captures knowledge about how to instantiate the abstract policy in a particular environmental context –e.g. turn my head to the left, use my left hand to reach, use an enveloping grasp...

4. L ABORATORY FOR P ERCEPTUAL R OBOTICS U NIVERSITY OF M ASSACHUSETTS A MHERST D EPARTMENT OF C OMPUTER S CIENCE Schema Theory Arbib (1995) describes control programs composed of: –Perceptual schema - a Ball might be characterized by “size,” “color,” “velocity,” etc. –Motor schema - actions characterized by a “degree of readiness” and “activity level.” Are such distinctions misleading? –Gibsonian Affordances: a perceptual feature is only meaningful if it facilitates action –Mirror Neurons: the same neurons will activate when performing an action or when observing someone else perform that action Claim: All perceptual information can come from appropriately designed controllers

5. L ABORATORY FOR P ERCEPTUAL R OBOTICS U NIVERSITY OF M ASSACHUSETTS A MHERST D EPARTMENT OF C OMPUTER S CIENCE How do we learn procedural structure? We would like the robot to differentiate its actions based on environmental context –e.g. Pick and Place Which available sensorimotor features are correlated –structure learning How these features relate, probabilistically, to each other –parameter learning

6. L ABORATORY FOR P ERCEPTUAL R OBOTICS U NIVERSITY OF M ASSACHUSETTS A MHERST D EPARTMENT OF C OMPUTER S CIENCE Relational Data Data with complex dependencies between instances or varying structure (not i.i.d.) Applicable to robotics domain because: –Different training episodes may exhibit varying structure Data designated as Objects and Attributes –Objects are related through the structure of the data –Attributes are related through learned statistical dependencies Relational Dependency Networks –approximate the full joint distribution of a set of variables with a set of conditional probability distributions –Perform Gibbs sampling to do joint inference

7. L ABORATORY FOR P ERCEPTUAL R OBOTICS U NIVERSITY OF M ASSACHUSETTS A MHERST D EPARTMENT OF C OMPUTER S CIENCE locale bounding box dimensions orientation convergence state lift-able fingers LocalizeReachGrasp convergence state Some Controller Objects

8. L ABORATORY FOR P ERCEPTUAL R OBOTICS U NIVERSITY OF M ASSACHUSETTS A MHERST D EPARTMENT OF C OMPUTER S CIENCE What is Relational About this Data? Reach Controller Grasp Controller Reach Controller Simple Assembly 1: Grasp Controller Assemble Controller

9. L ABORATORY FOR P ERCEPTUAL R OBOTICS U NIVERSITY OF M ASSACHUSETTS A MHERST D EPARTMENT OF C OMPUTER S CIENCE What is Relational About this Data? Reach Controller Grasp Controller Reach Controller Simple Assembly 2: Grasp Controller Assemble Controller Remanipulate Controller

10. L ABORATORY FOR P ERCEPTUAL R OBOTICS U NIVERSITY OF M ASSACHUSETTS A MHERST D EPARTMENT OF C OMPUTER S CIENCE Gathering the Dataset Observe an autonomous program or a teleoperator performing a task a variety of ways Each trial may follow a different trajectory Data is collected after each trial Model is learned with Proximity http://kdl.cs.umass.edu/proximity/

11. L ABORATORY FOR P ERCEPTUAL R OBOTICS U NIVERSITY OF M ASSACHUSETTS A MHERST D EPARTMENT OF C OMPUTER S CIENCE Experiments PickUp with Dexter TM 2 objects (3 orientations) tall box, coffee can 2 grasps: 2 VF, 3 VF 2 reaches: top approach side approach 8 locales uniformly distributed

12. L ABORATORY FOR P ERCEPTUAL R OBOTICS U NIVERSITY OF M ASSACHUSETTS A MHERST D EPARTMENT OF C OMPUTER S CIENCE locale bounding box dimensions orientation convergence state lift-able fingers LocalizeReachGrasp convergence state The Learned Model Graph

13. L ABORATORY FOR P ERCEPTUAL R OBOTICS U NIVERSITY OF M ASSACHUSETTS A MHERST D EPARTMENT OF C OMPUTER S CIENCE Attribute Trees The RDN algorithm estimates a CPD for each attribute –Learns a locally consistent Relational Probability Tree (RPT) for that attribute Each tree focuses attention on the most salient predictors of the corresponding attribute –Manages complexity –Allows for easy and intuitive interpretation –Each attribute (sensorimotor feature) has an affordance in terms of the current task

14. L ABORATORY FOR P ERCEPTUAL R OBOTICS U NIVERSITY OF M ASSACHUSETTS A MHERST D EPARTMENT OF C OMPUTER S CIENCE RPT for “Lift-able”

15. L ABORATORY FOR P ERCEPTUAL R OBOTICS U NIVERSITY OF M ASSACHUSETTS A MHERST D EPARTMENT OF C OMPUTER S CIENCE Using the RDN to construct policy How do we use the learned schema to perform the task again? –At each action point: perform joint inference on task success variables and find most likely resource assignment Use this assignment and see how likely success is Perform next action with resource binding, possibly uncovering new information through interaction

16. L ABORATORY FOR P ERCEPTUAL R OBOTICS U NIVERSITY OF M ASSACHUSETTS A MHERST D EPARTMENT OF C OMPUTER S CIENCE Yeah, but... how does it perform? Pick up the can with 2 or 3 fingers from the top Pick up the box with 2 fingers –From the side or the top standing up –From the top laying down Predicts little probability of success if object is outside reachable workspace

17. L ABORATORY FOR P ERCEPTUAL R OBOTICS U NIVERSITY OF M ASSACHUSETTS A MHERST D EPARTMENT OF C OMPUTER S CIENCE Where to Next? How do we learn the declarative structure? –Previous work by Huber, Platt, etc. Capture dynamic response of controllers during execution –Learn dependencies through direct interaction with the environment Can we sample a set attributes from uncountable possible set –Resample if poor policies are learned

18. L ABORATORY FOR P ERCEPTUAL R OBOTICS U NIVERSITY OF M ASSACHUSETTS A MHERST D EPARTMENT OF C OMPUTER S CIENCE The End

19. L ABORATORY FOR P ERCEPTUAL R OBOTICS U NIVERSITY OF M ASSACHUSETTS A MHERST D EPARTMENT OF C OMPUTER S CIENCE RDNs in Robotics What do we know? –a collection of controllers are necessary for a task, usually organized as a sequence of sub-goals –controllers have state, attached resources, and can reveal perceptual information through execution –controllers can execute sequentially or in conjunction What don’t we know? –Which sensorimotor features of each controller are important and how they correlate

20. L ABORATORY FOR P ERCEPTUAL R OBOTICS U NIVERSITY OF M ASSACHUSETTS A MHERST D EPARTMENT OF C OMPUTER S CIENCE Localize Reach Grasp Localize Reach Grasp Localize Reach Grasp Localize Reach Grasp Four Training Structures

21. L ABORATORY FOR P ERCEPTUAL R OBOTICS U NIVERSITY OF M ASSACHUSETTS A MHERST D EPARTMENT OF C OMPUTER S CIENCE What is Relational About this Data? Reach Controller Grasp Controller Localize Controller Reach Controller Obstacle Avoidance Controller Obstacle Avoidance Controller Kinematic Conditioning Controller Pick and Transport: Not independently distributed!!! sequential relations conjunctive relations