使用可感知感知的感知匹配维护可靠的世界模型
Maintaining a Reliable World Model using Action-aware Perceptual Anchoring
摘要：
可靠的感知对于与世界互动的机器人至关重要。但是传感器本身往往不足以提供这种能力，并且由于环境中的各种条件，它们容易出错。此外，机器人需要保持其周围环境的模型，即使当物体从视野中消失并且不再可见时。这需要将感知信息锚定到代表环境中对象的符号上。在本文中，我们提出了一个模型的行动感知锚定，使机器人跟踪对象在一个持久的方式。我们的基于规则的方法认为，归纳偏见执行高层次的推理结果从低层次的目标检测，它提高了机器人的感知能力，复杂的任务。我们评估了我们的模型对现有的基准模型的对象持久性，并表明它优于这些告密者本地化任务使用的数据集的1,371个视频。我们还整合了我们的行动感知感知锚定的认知架构的背景下，并展示了它的好处，在一个现实的变速箱装配任务的通用机器人。
英文摘要：
Reliable perception is essential for robots that interact with the world. But sensors alone are often insufficient to provide this capability, and they are prone to errors due to various conditions in the environment. Furthermore, there is a need for robots to maintain a model of its surroundings even when objects go out of view and are no longer visible. This requires anchoring perceptual information onto symbols that represent the objects in the environment. In this paper, we present a model for action-aware perceptual anchoring that enables robots to track objects in a persistent manner. Our rule-based approach considers inductive biases to perform high-level reasoning over the results from low-level object detection, and it improves the robot's perceptual capability for complex tasks. We evaluate our model against existing baseline models for object permanence and show that it outperforms these on a snitch localisation task using a dataset of 1,371 videos. We also integrate our action-aware perceptual anchoring in the context of a cognitive architecture and demonstrate its benefits in a realistic gearbox assembly task on a Universal Robot.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
