[精品]LearningFull-BodyMotionsfromMonocularVisionDynamicImitationinaHumanoidRobot.PDF
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1、Learning Full-Body Motions from Monocular Vision:Dynamic Imitation in a Humanoid RobotJeffrey B.Cole1David B.Grimes2Rajesh P.N.Rao2Department of Electrical Engineering1University of WashingtonBox 352500,Seattle,WA 98195 USAjeffcoleee.washington.eduDepartment of Computer Science andEngineering2Univer
2、sity of WashingtonBox 352350,Seattle,WA 98195 USAgrimes,raocs.washington.eduAbstractIn an effort to ease the burden of programmingmotor commands for humanoid robots,a computer vision tech-nique is developed for converting a monocular video sequenceof human poses into stabilized robot motor commands
3、for ahumanoid robot.The human teacher wears a multi-coloredbody suit while performing a desired set of actions.Leveragingthe colors of the body suit,the system detects the most probablelocations of the different body parts and joints in the image.Then,by exploiting the known dimensions of the body s
4、uit,a user specified number of candidate 3D poses are generatedfor each frame.Using human to robot joint correspondences,the estimated 3D poses for each frame are then mappedto corresponding robot motor commands.An initial set ofkinematically valid motor commands is generated using anapproximate bes
5、t path search through the pose candidatesfor each frame.Finally a learning-based probabilistic dynamicbalance model obtains a dynamically stable imitative sequenceof motor commands.We demonstrate the viability of theapproach by presenting results showing full-body imitation ofhuman actions by a Fuji
6、tsu HOAP-2 humanoid robot.I.INTRODUCTIONTeaching complex motor behavior to a robot can beextremely tedious and time consuming.Often,a programmerwill have to spend days deciding on exact motor controlsequences for every joint in the robot for a pose sequencethat only lasts a few seconds.A much more i
7、ntuitiveapproach would be to teach a robot how to generate itsown motor commands for gestures by simply watching aninstructor perform the desired task.In other words,the robotshould learn to translate the perceived pose of its instructorinto appropriate motor commands for itself.This imitationlearni
8、ng paradigm is intuitive because it is exactly how wehumans learn to control our bodies 1.Even at very youngages,we learn to control our bodies and perform tasks bywatching others perform those tasks.But the first hurdle inthis imitation learning task is one of image processing.Thechallenge is to de
9、velop accurate methods for extracting 3Dhuman poses from monocular image sequences.Imitation learning in humanoid and other robots has beenstudied in depth by a wide array of researchers.Early worksuch as 2,3 demonstrated the benefit of programminga robot via demonstration.Since then researchers hav
10、e ad-dressed building large corpora of useful skills 4,5,6,handling dynamics 7,8,studied biological connections9,or addressed goal-directed imitation 10.Typically a marker based motion capture system is usedto estimate human poses as input for training robots toperform complex motions.This requires
11、a full motion capturerig to extract the exact locations of special markers in arestricted 3D space.An instructor is typically required towear a special suit with careful marker placement.Themotion capture system then records the 3D position of eachmarker and recovers degree-of-freedom(DOF)estimatesr
12、elative to a skeletal model using various inverse kinematictechniques.Due to careful calibration of the cameras,highlyaccurate pose estimates can be extracted using multi-viewtriangulation techniques.The biggest downside to using a motion capture rig inour imitation learning scenario is that trainin
13、g can only beperformed in a rigid(and expensive)environment.Also,themotion capture system is unsatisfying because it does notallow the robot to behave autonomously.In this paper wedemonstrate initial steps in allowing the robot to use its ownvision system to extract the 3D pose of its instructor.Thi
14、swould allow us to”close the loop”for the learning process.Using only its own eyes,a robot should be able to watchan instructor,convert what it sees into a 3D pose,and thentranslate that sequence into appropriate motor commands.A large body of work has studied the problem performingpose estimation f
15、rom vision.Early computational approaches11,12 to analyzing images and video of people adoptedthe use of these kinematic models such as the kinematic treemodel.Since these earliest papers many systems have beenproposed for pose estimation and tracking(for examples see13,14,15,16),yet none have signi
16、ficantly supplantedmarker based motion capture for a broad array of applica-tions.The biggest limitation of many of these vision-based poseestimation techniques is that they require multiple,distantand often carefully calibrated cameras to be placed in aring around the instructor.While more portable
17、 and lesscostly than a commercial motion capture rig this is still notdesirable for autonomous robotic imitation learning.Thusin this paper we propose a method which relies solely onthe robots own commodity monocular camera.We note thatour work on monocular pose estimation builds on previousFig.2.RG
18、B training for body part detection.The top image shows handselected body part regions and the bottom plot shows each body parts colorclusters.techniques for solving the human and limb tracking problemusing learned image statistics 17,18,19,20,21.II.POSEESTIMATIONUSINGMONOCULARVIDEOAs an alternative
19、to expensive and cumbersome motioncapture systems,we have developed a new approach toestimating human poses using only a single,uncalibratedcamera and a multi-colored body suit.The method uses anonparametric probabilistic framework for localizing humanbody parts and joints in 2D images,converting th
20、ose jointsinto possible 3D locations,extracting the most likely 3Dpose,and then converting that pose into the equivalent motorcommands for our HOAP2 humanoid robot.As a final step,the motor commands are automatically refined to assurestability when the imitative action is finally performed bythe hum
21、anoid robot.The overall flow of the data processingis shown in Figure 1.A.Detecting Body Parts:The first step of the process is to detect where the differentbody parts are most likely located in each frame of the videosequence.Since we have granted ourselves the concession ofusing clothing with know
22、n colors,body part detection is doneby training a classifier in RGB color space.During the training phase,the user labels example regionsfor each of the body parts using a simple GUI.The RGBvalues of the pixels in each region are then fit with Gaussiandistributions and the curve fit parameters are s
23、aved to a file.An example of hand selected upper body parts and their RGBcolor clusters are shown in figure 2.Once the colors have been learned for each body part,itis relatively fast and easy to detect the probable body partFig.3.Probability map for the location of each upper body part in the given
24、frame.The value assigned to each pixel in the map is found by evaluatingthe pixels RGB values using the previously trained Gaussian distributions.Thus,intensity of the image on the right indicates the relative likelihood ofa pixel being a body part.Fig.4.Example of a probability map for the 2D locat
25、ions of each jointfor the video frame shown on the left.Joint maps are found by multiplyingtogether blurred versions of each of the body part maps.locations in any other frame from the sequence.For example,figure 3 shows the probability of each pixel being part ofthe persons torso,where intensity of
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