Mobile manipulators require coordinated control between navigation and manipulation to accomplish tasks. Typically, coordinated mobile manipulation behaviors have base navigation to approach the goal followed by arm manipulation to reach the desired pose. Selecting the embodiment between the base and arm can be determined based on reachability. Previous methods evaluate reachability by computing inverse kinematics and activate arm motions once solutions are identified. In this study, we introduce a new approach called predictive reachability that decides reachability based on predicted arm motions. Our model utilizes a hierarchical policy framework built upon a world model. The world model allows the prediction of future trajectories and the evaluation of reachability. The hierarchical policy selects the embodiment based on the predicted reachability and plans accordingly. Unlike methods that require prior knowledge about robots and environments for inverse kinematics, our method only relies on image-based observations. We evaluate our approach through basic reaching tasks across various environments. The results demonstrate that our method outperforms previous model-based approaches in both sample efficiency and performance, while enabling more reasonable embodiment selection based on predictive reachability.

翻译：移动操作机器人需要协调导航与操作控制以完成任务。通常，协调的移动操作行为包含：通过基座导航接近目标，随后通过机械臂操作达到期望位姿。基座与机械臂之间的本体选择可根据可达性确定。现有方法通过计算逆运动学评估可达性，并在获得解后激活机械臂运动。本研究提出一种称为预测可达性的新方法，该方法基于预测的机械臂运动判定可达性。我们的模型采用基于世界模型的分层策略框架。世界模型能够预测未来轨迹并评估可达性。分层策略根据预测的可达性选择本体并相应规划。与需要机器人及环境先验知识进行逆运动学计算的方法不同，本方法仅依赖基于图像的观测。我们通过在多种环境中执行基本到达任务来评估所提方法。结果表明，本方法在样本效率和性能上均优于先前的基于模型的方法，同时能够基于预测可达性实现更合理的本体选择。