论文标题
将基于学习的运动政策与基于模型的手机操作相结合
Combining Learning-based Locomotion Policy with Model-based Manipulation for Legged Mobile Manipulators
论文作者
论文摘要
深厚的强化学习为挑战地形提供了强大的运动政策。迄今为止，很少有研究利用基于模型的方法将这些运动技能与机械手的精确控制相结合。在这里，我们将外部动态计划纳入基于学习的移动操作政策中。我们通过在仿真中应用在机器人基础上的随机扳手序列并将噪声扳手序列预测添加到策略观察结果中来训练基本策略。然后，该政策学会抵消部分知名的未来干扰。随机扳手序列被从模型预测控制到启用部署的动力学计划生成的扳手预测所取代。我们在训练过程中显示出对操纵器的零射击改编。在硬件上，我们通过外部扳手的预测来演示腿部机器人的稳定运动。
Deep reinforcement learning produces robust locomotion policies for legged robots over challenging terrains. To date, few studies have leveraged model-based methods to combine these locomotion skills with the precise control of manipulators. Here, we incorporate external dynamics plans into learning-based locomotion policies for mobile manipulation. We train the base policy by applying a random wrench sequence on the robot base in simulation and adding the noisified wrench sequence prediction to the policy observations. The policy then learns to counteract the partially-known future disturbance. The random wrench sequences are replaced with the wrench prediction generated with the dynamics plans from model predictive control to enable deployment. We show zero-shot adaptation for manipulators unseen during training. On the hardware, we demonstrate stable locomotion of legged robots with the prediction of the external wrench.
