基于动作条件的深度视觉预测与RoAM：面向自主机器人的新型室内人类运动数据集
Action-conditioned Deep Visual Prediction with RoAM, a new Indoor Human Motion Dataset for Autonomous Robots
AI总结：
提出RoAM室内人类运动视频数据集及深度视觉预测框架ACPNet，通过结合机器人自我视角、LiDAR扫描与控制动作，实现基于动作条件的未来图像帧预测，推动自主导航研究。
摘要：
随着机器人在各行各业的日益普及，开发能够使机器人在与人类协作时有效预测、理解和规划其行动的高级算法变得至关重要。我们引入了Robot Autonomous Motion (RoAM)视频数据集，该数据集由定制的turtlebot3 Burger机器人在多种室内环境中收集，从机器人的自我视角记录了各种人类运动。该数据集还包含了LiDAR扫描的同步记录以及机器人在静态和移动的人类智能体周围导航时采取的所有控制动作。这一独特的数据集为开发和基准测试新的视觉预测框架提供了机会，这些框架能够在部分可观测场景或成像传感器安装在移动平台上的情况下，根据记录智能体采取的动作来预测未来的图像帧。我们在我们名为ACPNet的新型深度视觉预测框架上对该数据集进行了基准测试，其中近似生成的未来图像帧同样以机器人采取的动作为条件，并展示了其将机器人动力学纳入移动机器人和自主导航研究的视频预测范式的潜力。
英文摘要：
With the increasing adoption of robots across industries, it is crucial to focus on developing advanced algorithms that enable robots to anticipate, comprehend, and plan their actions effectively in collaboration with humans. We introduce the Robot Autonomous Motion (RoAM) video dataset, which is collected with a custom-made turtlebot3 Burger robot in a variety of indoor environments recording various human motions from the robot's ego-vision. The dataset also includes synchronized records of the LiDAR scan and all control actions taken by the robot as it navigates around static and moving human agents. The unique dataset provides an opportunity to develop and benchmark new visual prediction frameworks that can predict future image frames based on the action taken by the recording agent in partially observable scenarios or cases where the imaging sensor is mounted on a moving platform. We have benchmarked the dataset on our novel deep visual prediction framework called ACPNet where the approximated future image frames are also conditioned on action taken by the robot and demonstrated its potential for incorporating robot dynamics into the video prediction paradigm for mobile robotics and autonomous navigation research.
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