Recent years have witnessed many successful trials in the robot learning field. For contact-rich robotic tasks, it is challenging to learn coordinated motor skills by reinforcement learning. Imitation learning solves this problem by using a mimic reward to encourage the robot to track a given reference trajectory. However, imitation learning is not so efficient and may constrain the learned motion. In this paper, we propose instruction learning, which is inspired by the human learning process and is highly efficient, flexible, and versatile for robot motion learning. Instead of using a reference signal in the reward, instruction learning applies a reference signal directly as a feedforward action, and it is combined with a feedback action learned by reinforcement learning to control the robot. Besides, we propose the action bounding technique and remove the mimic reward, which is shown to be crucial for efficient and flexible learning. We compare the performance of instruction learning with imitation learning, indicating that instruction learning can greatly speed up the training process and guarantee learning the desired motion correctly. The effectiveness of instruction learning is validated through a bunch of motion learning examples for a biped robot and a quadruped robot, where skills can be learned typically within several million steps. Besides, we also conduct sim-to-real transfer and online learning experiments on a real quadruped robot. Instruction learning has shown great merits and potential, making it a promising alternative for imitation learning.

翻译：近年来，机器人学习领域涌现出许多成功的尝试。对于接触密集型的机器人任务，通过强化学习协调的运动技能颇具挑战性。模仿学习通过使用模仿奖励鼓励机器人跟踪给定参考轨迹解决了这一问题。然而，模仿学习效率不高，且可能限制学习到的运动模式。本文提出指令学习，该方法受人类学习过程启发，在机器人运动学习中展现出高效性、灵活性与通用性。指令学习不将参考信号用于奖励函数，而是直接将其作为前馈动作，并与强化学习得到的反馈动作相结合来控制机器人。此外，我们提出了动作约束技术并移除了模仿奖励，这对实现高效灵活的学习至关重要。我们比较了指令学习与模仿学习的性能，表明指令学习能显著加速训练过程，并确保正确习得期望的运动。通过在双足机器人和四足机器人的一系列运动学习实例验证，技能通常能在百万步内习得。此外，我们还在真实四足机器人上进行了仿真到现实迁移和在线学习实验。指令学习展现了显著优势与潜力，有望成为模仿学习的替代方案。