The ability to learn new tasks and quickly adapt to different variations or dimensions is an important attribute in agile robotics. In our previous work, we have explored Behavior Trees and Motion Generators (BTMGs) as a robot arm policy representation to facilitate the learning and execution of assembly tasks. The current implementation of the BTMGs for a specific task may not be robust to the changes in the environment and may not generalize well to different variations of tasks. We propose to extend the BTMG policy representation with a module that predicts BTMG parameters for a new task variation. To achieve this, we propose a model that combines a Gaussian process and a weighted support vector machine classifier. This model predicts the performance measure and the feasibility of the predicted policy with BTMG parameters and task variations as inputs. Using the outputs of the model, we then construct a surrogate reward function that is utilized within an optimizer to maximize the performance of a task over BTMG parameters for a fixed task variation. To demonstrate the effectiveness of our proposed approach, we conducted experimental evaluations on push and obstacle avoidance tasks in simulation and with a real KUKA iiwa robot. Furthermore, we compared the performance of our approach with four baseline methods.

翻译：具备学习新任务并快速适应不同变化或维度的能力是敏捷机器人的重要属性。在先前工作中，我们探索了将行为树与运动生成器（BTMGs）作为机器人手臂策略表征，以促进装配任务的学习与执行。当前针对特定任务的BTMGs实现在面对环境变化时可能缺乏鲁棒性，且难以泛化至不同任务变体。为此，我们提出扩展BTMGs策略表征，新增一个模块用于预测新任务变体的BTMGs参数。具体实现中，我们提出了一种结合高斯过程与加权支持向量机分类器的混合模型。该模型以BTMGs参数和任务变体为输入，预测策略的性能指标与可行性。通过模型输出，我们构建替代奖励函数，并利用优化器在固定任务变体下最大化BTMGs参数对应的任务性能。为验证所提方法的有效性，我们在仿真环境及真实KUKA iiwa机器人上开展了推箱和避障任务的实验评估。此外，我们将该方法与四种基准方法的性能进行了对比分析。