标题： 通过自监督RNN实现生物启发式机器人轨迹规划 显示英文标题

标题： Towards Bio-Inspired Robotic Trajectory Planning via Self-Supervised RNN

摘要： 机器人轨迹规划被理解为生成一系列关节配置，使机器人代理或其机械臂从初始状态到达期望的最终状态，从而在考虑机器人运动学和环境约束的情况下完成操作任务。 通常，这是通过基于采样的规划器实现的，这计算量较大。 最近的进展表明，轨迹规划也可以通过轨迹的监督序列学习来完成，通常只需要通过神经架构一次或固定次数，从而确保计算时间有限。 然而，这种完全监督的方法进行的是模仿学习；它们不是根据轨迹是否能成功到达目标来学习，而是试图复制观察到的轨迹。 在我们的工作中，我们在此方法的基础上，提出了一种基于循环架构的认知启发式自监督学习方案，用于构建轨迹模型。 我们在一个机械臂的运动学规划任务上评估了所提出方法的可行性。 结果表明，该模型能够仅使用给定的正向和逆向运动学模型对来学习生成轨迹，并表明这种新方法可能有助于需要自适应解决方案的更复杂的操作任务的规划。 显示英文摘要

摘要： Trajectory planning in robotics is understood as generating a sequence of joint configurations that will lead a robotic agent, or its manipulator, from an initial state to the desired final state, thus completing a manipulation task while considering constraints like robot kinematics and the environment. Typically, this is achieved via sampling-based planners, which are computationally intensive. Recent advances demonstrate that trajectory planning can also be performed by supervised sequence learning of trajectories, often requiring only a single or fixed number of passes through a neural architecture, thus ensuring a bounded computation time. Such fully supervised approaches, however, perform imitation learning; they do not learn based on whether the trajectories can successfully reach a goal, but try to reproduce observed trajectories. In our work, we build on this approach and propose a cognitively inspired self-supervised learning scheme based on a recurrent architecture for building a trajectory model. We evaluate the feasibility of the proposed method on a task of kinematic planning for a robotic arm. The results suggest that the model is able to learn to generate trajectories only using given paired forward and inverse kinematics models, and indicate that this novel method could facilitate planning for more complex manipulation tasks requiring adaptive solutions.