We propose a learning-based system for enabling quadrupedal robots to manipulate large, heavy objects using their whole body. Our system is based on a hierarchical control strategy that uses the deep latent variable embedding which captures manipulation-relevant information from interactions, proprioception, and action history, allowing the robot to implicitly understand object properties. We evaluate our framework in both simulation and real-world scenarios. In the simulation, it achieves a success rate of 93.6 % in accurately re-positioning and re-orienting various objects within a tolerance of 0.03 m and 5 {\deg}. Real-world experiments demonstrate the successful manipulation of objects such as a 19.2 kg water-filled drum and a 15.3 kg plastic box filled with heavy objects while the robot weighs 27 kg. Unlike previous works that focus on manipulating small and light objects using prehensile manipulation, our framework illustrates the possibility of using quadrupeds for manipulating large and heavy objects that are ungraspable with the robot's entire body. Our method does not require explicit object modeling and offers significant computational efficiency compared to optimization-based methods. The video can be found at https://youtu.be/fO_PVr27QxU.

翻译：我们提出了一种基于学习的系统，使四足机器人能够利用其全身操控大型重型物体。该系统基于分层控制策略，通过深度潜变量嵌入从交互、本体感知和动作历史中捕捉与操控相关的信息，使机器人能够隐式理解物体特性。我们在仿真和实际场景中评估了该框架。在仿真中，系统在精确重定位和重定向各类物体时，在0.03米和5度容差范围内实现了93.6%的成功率。实际实验成功操控了重达19.2公斤的装满水的滚筒以及装满重物的15.3公斤塑料箱，而机器人自身仅重27公斤。与以往聚焦于通过抓取式操控处理小型轻质物体的研究不同，我们的框架展示了利用四足机器人以全身方式操控难以抓取的大型重型物体的可能性。该方法无需显式物体建模，且相比基于优化的方法具有显著的计算效率优势。相关视频可参见https://youtu.be/fO_PVr27QxU。