CVPR Daily - Thursday

right by a specified angle. In this work, the agent was equipped with an RGB-D camera mounted at a height of 0.88m and tilted -20 ° . Camera’s resolution was 360x640 pixels with 70 ° horizontal field of view and base radius of 0.18m. PointNav comes under two versions: v1) idealized setting: the agent is equipped with noise-free camera and access to ground-truth localization and movement is deterministic. In idealized setting, with no noise, map-less navigation models trained with large-scale reinforcement learning achieve 100% success. State-of-the-art approaches seem to have solved this problem; v2) realistic setting, where the agent must deal with actuation and sensing noise, and lack of high-precision localization in indoor environments. This is considered yet an unsolved challenge. In all experiments, the agent is evaluated via three primary metrics. 1) Success , where an episode is considered successful if the agent issues the stop command within 0.36m (2 × agent radius) of the goal. 2) Success weight by (inverse normalized) Path Length (SPL), where success is weighted by the efficiency of the agent’s path, calculated considering the geodesic distance (shortest path). 3) SoftSPL , where the binary success is replaced by progress towards the goal. 4 DAILY CVPR Thursday Poster Presentation