NDLI: Learning to reach into the unknown: Selecting initial conditions when reaching in clutter

Content Provider	IEEE Xplore Digital Library
Author	Daehyung Park Kapusta, A. You Keun Kim Rehg, J.M. Kemp, C.C.
Copyright Year	2014
Description	Author affiliation: Comput. Perception Lab., Georgia Inst. of Technol., Atlanta, GA, USA (Rehg, J.M.) \|\| Healthcare Robot. Lab., Georgia Inst. of Technol., Atlanta, GA, USA (Daehyung Park; Kapusta, A.; You Keun Kim; Kemp, C.C.)
Abstract	Often in highly-cluttered environments, a robot can observe the exterior of the environment with ease, but cannot directly view nor easily infer its detailed internal structure (e.g., dense foliage or a full refrigerator shelf). We present a data-driven approach that greatly improves a robot's success at reaching to a goal location in the unknown interior of an environment based on observable external properties, such as the category of the clutter and the locations of openings into the clutter (i.e., apertures). We focus on the problem of selecting a good initial configuration for a manipulator when reaching with a greedy controller. We use density estimation to model the probability of a successful reach given an initial condition and then perform constrained optimization to find an initial condition with the highest estimated probability of success. We evaluate our approach with two simulated robots reaching in clutter, and provide a demonstration with a real PR2 robot reaching to locations through random apertures. In our evaluations, our approach significantly outperformed two alternative approaches when making two consecutive reach attempts to goals in distinct categories of unknown clutter. Our approach only uses sparse readily-apparent features.
Starting Page	630
Ending Page	637
File Size	5075375
Page Count	8
File Format	PDF
e-ISBN	9781479969340
DOI	10.1109/IROS.2014.6942625
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clutter End effectors Apertures Optimization Robot sensing systems
Content Type	Text
Resource Type	Article

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