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Bridging Scene Understanding and Task Execution with Flexible Simulation Environments | | Zachary Ravichandran
; J. Daniel Griffith
; Benjamin Smith
; Costas Frost
; | | Date: |
20 Nov 2020 | | Abstract: | Significant progress has been made in scene understanding which seeks to
build 3D, metric and object-oriented representations of the world.
Concurrently, reinforcement learning has made impressive strides largely
enabled by advances in simulation. Comparatively, there has been less focus in
simulation for perception algorithms. Simulation is becoming increasingly vital
as sophisticated perception approaches such as metric-semantic mapping or 3D
dynamic scene graph generation require precise 3D, 2D, and inertial information
in an interactive environment. To that end, we present TESSE (Task Execution
with Semantic Segmentation Environments), an open source simulator for
developing scene understanding and task execution algorithms. TESSE has been
used to develop state-of-the-art solutions for metric-semantic mapping and 3D
dynamic scene graph generation. Additionally, TESSE served as the platform for
the GOSEEK Challenge at the International Conference of Robotics and Automation
(ICRA) 2020, an object search competition with an emphasis on reinforcement
learning. Code for TESSE is available at this https URL. | | Source: | arXiv, 2011.10452 | | Services: | Forum | Review | PDF | Favorites | |
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