Simulation enables robots to plan and estimate the outcomes of prospective actions without the need to physically execute them. We introduce a self-supervised learning framework to enable robots model and predict their morphology, kinematics and motor control using only brief raw video data, eliminating the need for extensive real-world data collection and kinematic priors. By observing their own movements, akin to humans watching their reflection in a mirror, robots learn an ability to simulate themselves and predict their spatial motion for various tasks. Our results demonstrate that this self-learned simulation not only enables accurate motion planning but also allows the robot to detect abnormalities and recover from damage.

翻译：仿真使机器人能够在无需实际执行的情况下，规划和估计预期动作的结果。我们提出了一种自监督学习框架，使机器人能够仅通过简短的原始视频数据来建模并预测其形态、运动学和电机控制，从而消除了对大量真实世界数据收集和运动学先验的需求。通过观察自身运动（类似于人类观察镜中自己的反射），机器人学会了一种模拟自身并预测其在不同任务中空间运动的能力。我们的结果表明，这种自学习模拟不仅能够实现精确的运动规划，还能使机器人检测异常并从损伤中恢复。