移动操作行为中用于具身选择的预测可达性
Predictive Reachability for Embodiment Selection in Mobile Manipulation Behaviors
AI总结：
针对移动操作中底盘与机械臂的具身选择问题，提出基于世界模型和分层策略的预测可达性方法，仅凭图像观测即可预测机械臂轨迹并决策，提升样本效率、性能与选择合理性。
摘要：
移动操作机器人需要导航与操作之间的协调控制才能完成任务。通常，协调的移动操作行为先由底盘导航接近目标，随后由机械臂操作以到达期望位姿。在底盘与机械臂之间选择具身，可以根据可达性来决定。以往方法通过计算逆运动学来评估可达性，并在找到解后启动机械臂运动。在本研究中，我们提出一种称为预测可达性的新方法，它根据预测的机械臂运动来判定可达性。我们的模型采用建立在世界模型之上的分层策略框架。该世界模型能够预测未来轨迹并评估可达性。分层策略根据预测可达性选择具身，并据此进行规划。不同于需要有关机器人和环境的先验知识以进行逆运动学计算的方法，我们的方法仅依赖基于图像的观测。我们通过跨多种环境的基础到达任务评估了该方法。结果表明，我们的方法在样本效率和性能两方面均优于以往基于模型的方法，同时能够基于预测可达性实现更合理的具身选择。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
