论文标题
闭塞意识搜索杂物中的对象检索
Occlusion-Aware Search for Object Retrieval in Clutter
论文作者
论文摘要
我们解决了从混乱的架子中检索目标对象的操纵任务。当目标对象被隐藏时，机器人必须通过混乱搜索以检索其。解决此任务需要在目标对象的可能位置进行推理。它还需要关于多对象相互作用和未来遮挡的物理推理。在这项工作中，我们提出了一个数据驱动的混合计划器，用于在闭环中生成闭塞感。杂种计划者探索了从观测流中学分布所预测的遮挡目标对象的可能位置。该搜索是由接受强化学习的启发式训练来指导的，以闭塞的观察行动。我们在不同的模拟和现实世界设置中评估了我们的方法（视频在https://youtu.be/dy7yq3luvqg上可用）。结果验证了我们的方法可以在现实世界中几乎实时搜索和检索目标对象，同时仅接受模拟培训。
We address the manipulation task of retrieving a target object from a cluttered shelf. When the target object is hidden, the robot must search through the clutter for retrieving it. Solving this task requires reasoning over the likely locations of the target object. It also requires physics reasoning over multi-object interactions and future occlusions. In this work, we present a data-driven hybrid planner for generating occlusion-aware actions in closed-loop. The hybrid planner explores likely locations of the occluded target object as predicted by a learned distribution from the observation stream. The search is guided by a heuristic trained with reinforcement learning to act on observations with occlusions. We evaluate our approach in different simulation and real-world settings (video available on https://youtu.be/dY7YQ3LUVQg). The results validate that our approach can search and retrieve a target object in near real time in the real world while only being trained in simulation.
