The objective of this work is to augment the basic abilities of a robot by learning to use sensorimotor primitives to solve complex long-horizon manipulation problems. This requires flexible generative planning that can combine primitive abilities in novel combinations and, thus, generalize across a wide variety of problems. In order to plan with primitive actions, we must have models of the actions: under what circumstances will executing this primitive successfully achieve some particular effect in the world? We use, and develop novel improvements to, state-of-the-art methods for active learning and sampling. We use Gaussian process methods for learning the constraints on skill effectiveness from small numbers of expensive-to-collect training examples. In addition, we develop efficient adaptive sampling methods for generating a comprehensive and diverse sequence of continuous candidate control parameter values (such as pouring waypoints for a cup) during planning. These values become end-effector goals for traditional motion planners that then solve for a full robot motion that performs the skill. By using learning and planning methods in conjunction, we take advantage of the strengths of each and plan for a wide variety of complex dynamic manipulation tasks. We demonstrate our approach in an integrated system, combining traditional robotics primitives with our newly learned models using an efficient robot task and motion planner. We evaluate our approach both in simulation and in the real world through measuring the quality of the selected primitive actions. Finally, we apply our integrated system to a variety of long-horizon simulated and real-world manipulation problems.

这项工作的目的是通过学习使用感觉运动原语来解决复杂的长水平操纵问题，从而增强机器人的基本能力。这就需要灵活的生成计划，该计划可以将原始能力组合成新颖的组合，从而概括出各种各样的问题。为了计划原始动作，我们必须具有动作模型：在什么情况下执行该原始动作将在世界上成功实现某些特定效果？我们使用最先进的方法进行主动学习和采样，并对它们进行新颖的改进。我们使用高斯过程方法从少量昂贵的收集训练示例中学习对技能有效性的约束。此外，我们开发了有效的自适应采样方法，以在规划过程中生成全面而多样的连续候选控制参数值序列（例如倒杯的路标）。这些值成为传统运动计划者的最终执行者目标，然后他们解决了执行该技能的完整机器人运动。通过结合使用学习和计划方法，我们可以充分利用每种方法的优势，并计划进行各种复杂的动态操纵任务。我们在一个集成系统中演示了我们的方法，该方法使用高效的机器人任务和运动计划器将传统的机器人原语与新近​​学习的模型相结合。通过评估所选原始动作的质量，我们在仿真和现实世界中都评估了我们的方法。最后，