Search Results for author:
Found 3 papers, 2 papers with code
Habitat Synthetic Scenes Dataset (HSSD-200): An Analysis of 3D Scene Scale and Realism Tradeoffs for ObjectGoal Navigation
Surprisingly, we observe that agents trained on just 122 scenes from our dataset outperform agents trained on 10, 000 scenes from the ProcTHOR-10K dataset in terms of zero-shot generalization in real-world scanned environments.
Megaverse: Simulating Embodied Agents at One Million Experiences per Second
We present Megaverse, a new 3D simulation platform for reinforcement learning and embodied AI research.
Large Batch Simulation for Deep Reinforcement Learning
We accelerate deep reinforcement learning-based training in visually complex 3D environments by two orders of magnitude over prior work, realizing end-to-end training speeds of over 19, 000 frames of experience per second on a single GPU and up to 72, 000 frames per second on a single eight-GPU machine.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
