Exploitation-Guided Exploration for Semantic Embodied Navigation
Justin Wasserman Girish Chowdhary Abhinav Gupta Unnat Jain
In the recent progress in embodied navigation and sim-to-robot transfer,
modular policies have emerged as a de facto framework. However, there is more
to compositionality beyond the decomposition of the learning load into modular
components. In this work, we investigate a principled way to syntactically
combine these components. Particularly, we propose Exploitation-Guided
Exploration (XGX) where separate modules for exploration and exploitation come
together in a novel and intuitive manner. We configure the exploitation module
to take over in the deterministic final steps of navigation i.e. when the goal
becomes visible. Crucially, an exploitation module teacher-forces the
exploration module and continues driving an overridden policy optimization.
XGX, with effective decomposition and novel guidance, improves the
state-of-the-art performance on the challenging object navigation task from 70%
to 73%. Along with better accuracy, through targeted analysis, we show that XGX
is also more efficient at goal-conditioned exploration. Finally, we show
sim-to-real transfer to robot hardware and XGX performs over two-fold better
than the best baseline from simulation benchmarking. Project page:
xgxvisnav.github.io