论文标题
部分可观测时空混沌系统的无模型预测
Long-HOT: A Modular Hierarchical Approach for Long-Horizon Object Transport
论文作者
论文摘要
我们通过提出新的对象传输任务以及一个新颖的模块化框架来解决长期体现的探索和导航中的关键挑战。我们的第一个贡献是设计一种新型的长期奔放环境，专注于深度探索和长途计划，在该计划中，代理需要有效地找到和拾取目标位置的目标对象，并在目标位置携带和掉落，并在容器中找到一个容器，如果找到一个容器。此外，我们提出了一个模块化的分层运输政策（HTP），该政策构建了场景的拓扑图，以借助加权边界进行探索。我们的分层方法结合了运动计划算法，以在探索的位置和对象导航策略中达到点目标，以朝着未知位置朝着语义目标迈进。我们提出的栖息地运输任务和多基准测试的实验表明，我们的方法显着超过了基准和先前的工作。此外，我们通过仅对更简单的任务版本进行培训来证明对更艰难的运输场景的有意义的概括，从而验证了模块化方法对长马运输的有效性。
We address key challenges in long-horizon embodied exploration and navigation by proposing a new object transport task and a novel modular framework for temporally extended navigation. Our first contribution is the design of a novel Long-HOT environment focused on deep exploration and long-horizon planning where the agent is required to efficiently find and pick up target objects to be carried and dropped at a goal location, with load constraints and optional access to a container if it finds one. Further, we propose a modular hierarchical transport policy (HTP) that builds a topological graph of the scene to perform exploration with the help of weighted frontiers. Our hierarchical approach uses a combination of motion planning algorithms to reach point goals within explored locations and object navigation policies for moving towards semantic targets at unknown locations. Experiments on both our proposed Habitat transport task and on MultiOn benchmarks show that our method significantly outperforms baselines and prior works. Further, we validate the effectiveness of our modular approach for long-horizon transport by demonstrating meaningful generalization to much harder transport scenes with training only on simpler versions of the task.
