Robotic assembly tasks involve complex and low-clearance insertion trajectories with varying contact forces at different stages. While the nominal motion trajectory can be easily obtained from human demonstrations through kinesthetic teaching, teleoperation, simulation, among other methods, the force profile is harder to obtain especially when a real robot is unavailable. It is difficult to obtain a realistic force profile in simulation even with physics engines. Such simulated force profiles tend to be unsuitable for the actual robotic assembly due to the reality gap and uncertainty in the assembly process. To address this problem, we present a combined learning-based framework to imitate human assembly skills through hybrid trajectory learning and force learning. The main contribution of this work is the development of a framework that combines hierarchical imitation learning, to learn the nominal motion trajectory, with a reinforcement learning-based force control scheme to learn an optimal force control policy, that can satisfy the nominal trajectory while adapting to the force requirement of the assembly task. To further improve the imitation learning part, we develop a hierarchical architecture, following the idea of goal-conditioned imitation learning, to generate the trajectory learning policy on the \textit{skill} level offline. Through experimental validations, we corroborate that the proposed learning-based framework is robust to uncertainty in the assembly task, can generate high-quality trajectories, and can find suitable force control policies, which adapt to the task's force requirements more efficiently.
翻译:机器人组装任务涉及复杂和低清的插入轨迹,在不同阶段有不同的接触力量。虽然名义运动轨迹可以很容易地通过动画教学、远程操作、模拟等方法从人类演示中获取,但力量轮廓更难获得,特别是在没有真正的机器人的情况下;即使使用物理引擎,也很难在模拟中获得现实的武力轮廓。这种模拟部队轮廓由于在组装过程中存在现实差距和不确定性,往往不适合实际的机器人组装。为了解决这一问题,我们提出了一个基于学习的综合框架,以通过混合轨迹学习和部队学习来模仿人类组装技能。这项工作的主要贡献是开发一个框架,将等级模仿学习结合起来,学习名义运动轨迹,学习一个基于学习的机械化部队控制计划,以学习最佳部队控制政策,这既能满足名义的轨迹,又能适应组装任务的要求。为了进一步改进模拟学习部分,我们根据以目标为基础的模拟学习理念,制定了一个基于轨迹学系的架构,以生成在\ Text killy} 水平上的轨迹学习,我们通过实验性校准高质的校准, 校准高级的校准,可以验证高级的校装要求。 通过实验性校准,我们的校正的校正的校装要求可以建立到高级的校装。