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DevGRU: Depth-guided Visual Navigation using a Collision-aware Recurrent Model

INVESTOR TAKEAWAY

Existing visual navigation models often aim to develop foundation models that can generalize robot navigation across diverse platforms. However, many of these models are prone to collisions when deployed in complex indoor environments, particularly in structured layouts and narrow passages.

ORIGINAL PAPER

DevGRU: Depth-guided Visual Navigation using a Collision-aware Recurrent Model

WHAT WE KNOW

To address this problem, we propose a depth image- and point-goal-conditioned navigation system, DevGRU. The proposed system employs an action predictor (AP) that generates collision-aware future trajectories, enabling effective avoidance of immediate obstacles. In conjunction with a collision predictor, the AP further compensates for errors accumulated in the goal pose estimation and proactively mitigates future deviations.

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