Name
Abstract | ||
|---|---|---|
This paper discusses an integrated model of a robot's sensory and perceptual capabilities based on one of the earliest forms of self-knowledge that humans develop, knowledge of the Ecological Self. The Ecological Self is a cohesive model of the body and senses learned through the experience of using them together. This unified model allows kinematic and sensory data to be combined, producing an intersensory perception grounded in both inputs. Taking inspiration from this Ecological Self, but building on modern engineering practices, this model allows a robot to learn the kinematics of its end-effector by witnessing its motion in its visual field. This property of adaptation through self-observation also allows the model to adapt to changes in the robot's kinematic structure, as in the case of tool use. A final refinement is performed over the combined visual-kinematic model and is demonstrated to improve not only the accuracy of the kinematic model, but also the robot's stereo vision calibration. This refinement is inspired by the hypothetical process by which infants learn about their selves. The system is demonstrated to require fewer than 200 motion samples to fully train, to predict end-effector position within 2.29mm (SD=0.10) and 2.93 pixels (SD=3.83), to learn the lengths of the linkages in the robot's arm to within 1.1mm, and to adapt to tool use after only 52 samples. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/Humanoids.2011.6100897 | Humanoid Robots |
Keywords | Field | DocType |
ecology,end effectors,psychology,robot vision,stereo image processing,ecological self,end-effector,intersensory perception,robotic model,stereo vision calibration,visual-kinematic model | Stereo cameras,Linkage (mechanical),Kinematics,Computer science,Simulation,Stereopsis,Robot end effector,Robot,Perception,Ecological self | Conference |
ISSN | ISBN | Citations |
2164-0572 E-ISBN : 978-1-61284-867-9 | 978-1-61284-867-9 | 2 |
PageRank | References | Authors |
0.41 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Justin W. Hart | 1 | 191 | 15.23 |
| B. Scassellati | 2 | 1735 | 241.11 |