SCOD：基于动作序列感知交换性的具身智能体主动目标检测
SCOD: Active Object Detection for Embodied Agents using Sensory Commutativity of Action Sequences
摘要：
介绍了一种用于运动和不运动目标检测的主动式方法SCOD（Sensory Commutativity目标检测）。SCOD利用动作序列的可交换属性，在配备第一人称传感器和具有多个自由度的连续运动空间的具体代理的情况下。SCOD基于从同一起点以两种不同顺序播放动作序列，并比较每个序列后获得的两个最终观察结果。我们的3D逼真的机器人设置（iGibson）的实验证明了SCOD的准确性和它的推广看不见的环境和对象。我们还成功地应用SCOD在一个真正的机器人，以进一步说明其泛化性能。与SCOD，我们的目标是提供一种新的方式来接近的对象发现的问题，在一个天真的体现代理的上下文中。我们提供代码和补充视频。
英文摘要：
We introduce SCOD (Sensory Commutativity Object Detection), an active method for movable and immovable object detection. SCOD exploits the commutative properties of action sequences, in the scenario of an embodied agent equipped with first-person sensors and a continuous motor space with multiple degrees of freedom. SCOD is based on playing an action sequence in two different orders from the same starting point and comparing the two final observations obtained after each sequence. Our experiments on 3D realistic robotic setups (iGibson) demonstrate the accuracy of SCOD and its generalization to unseen environments and objects. We also successfully apply SCOD on a real robot to further illustrate its generalization properties. With SCOD, we aim at providing a novel way of approaching the problem of object discovery in the context of a naive embodied agent. We provide code and a supplementary video.
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。
