TY - GEN
T1 - Deep reinforcement learning methods for navigational aids
AU - Fakhri, Bijan
AU - Keech, Aaron
AU - Schlosser, Joel
AU - Brooks, Ethan
AU - Demakethepalli Venkateswara, Hemanth
AU - Panchanathan, Sethuraman
AU - Kira, Zsolt
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - Navigation is one of the most complex daily activities we engage in. Partly due to its complexity, navigational abilities are vulnerable to many conditions including Topographical Agnosia, Alzheimer’s Disease, and vision impairments. While navigation using solely vision remains a difficult problem in the field of assistive technology, emerging methods in Deep Reinforcement Learning and Computer Vision show promise in producing vision-based navigational aids for those with navigation impairments. To this effect, we introduce GraphMem, a Neural Computing approach to navigation tasks and compare it to several state of the art Neural Computing methods in a one-shot, 3D, first-person maze solving task. Comparing GraphMem to current methods in navigation tasks unveils insights into navigation and represents a first step towards employing these emerging techniques in navigational assistive technology.
AB - Navigation is one of the most complex daily activities we engage in. Partly due to its complexity, navigational abilities are vulnerable to many conditions including Topographical Agnosia, Alzheimer’s Disease, and vision impairments. While navigation using solely vision remains a difficult problem in the field of assistive technology, emerging methods in Deep Reinforcement Learning and Computer Vision show promise in producing vision-based navigational aids for those with navigation impairments. To this effect, we introduce GraphMem, a Neural Computing approach to navigation tasks and compare it to several state of the art Neural Computing methods in a one-shot, 3D, first-person maze solving task. Comparing GraphMem to current methods in navigation tasks unveils insights into navigation and represents a first step towards employing these emerging techniques in navigational assistive technology.
KW - Assistive technology
KW - Navigation
KW - Reinforcement learning
KW - Topographical agnosia
UR - http://www.scopus.com/inward/record.url?scp=85058549481&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058549481&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-04375-9_6
DO - 10.1007/978-3-030-04375-9_6
M3 - Conference contribution
AN - SCOPUS:85058549481
SN - 9783030043742
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 66
EP - 75
BT - Smart Multimedia - 1st International Conference, ICSM 2018, Revised Selected Papers
A2 - Berretti, Stefano
A2 - Basu, Anup
PB - Springer Verlag
T2 - 1st International Conference on Smart Multimedia, ICSM 2018
Y2 - 24 August 2018 through 26 August 2018
ER -