GibsonEnv: Embodied Real-World Active Perception


Perception and being active (i.e. having a certain level of motion freedom) are closely tied. Learning active perception and sensorimotor control in the physical world is cumbersome as existing algorithms are too slow to efficiently learn in real-time and robots are fragile and costly. This has given rise to learning in simulation which consequently casts a question on transferring to real-world. In this paper, we study learning perception for active agents in real-world, propose a virtual environment for this purpose, and demonstrate complex learned locomotion abilities. The primary characteristics of the learning environments, which transfer into the trained agents, are I) being from the real-world and reflecting its semantic complexity, II) having a mechanism to ensure no need to further domain adaptation prior to deployment of results in real-world, III) embodiment of the agent and making it subject to constraints of space and physics.

In Conference on Computer Vision and Pattern Recognition, IEEE
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.
Click the Slides button above to demo Academic’s Markdown slides feature.

Supplementary notes can be added here, including code and math.

Alexander Sax
Alexander Sax
PhD Student, Computer Science

My research interests include distributed robotics, mobile computing and programmable matter.