The robot position speculation, which determines where the chassis should move, is one key step to control the mobile manipulators. The target position must ensure the feasibility of chassis movement and manipulability, which is guaranteed by randomized sampling and kinematic checking in traditional methods. Addressing the demands of agile robotics, this paper proposes a robot position speculation network(RPSN), a learning-based approach to enhance the agility of mobile manipulators. The RPSN incorporates a differentiable inverse kinematic algorithm and a neural network. Through end-to-end training, the RPSN can speculate positions with a high success rate. We apply the RPSN to mobile manipulators disassembling end-of-life electric vehicle batteries (EOL-EVBs). Extensive experiments on various simulated environments and physical mobile manipulators demonstrate that the probability of the initial position provided by RPSN being the ideal position is 96.67%. From the kinematic constraint perspective, it achieves 100% generation of the ideal position on average within 1.28 attempts. Much lower than that of random sampling, 31.04. Moreover, the proposed method demonstrates superior data efficiency over pure neural network approaches. The proposed RPSN enables the robot to quickly infer feasible target positions by intuition. This work moves towards building agile robots that can act swiftly like humans.

翻译：机器人位置推测是决定底盘移动方向的关键步骤，对于控制移动机械臂至关重要。目标位置必须确保底盘运动的可行性和可操作性，传统方法通过随机采样和运动学校验来保证这一点。为满足敏捷机器人的需求，本文提出了一种基于学习的机器人位置推测网络（RPSN），旨在增强移动机械臂的敏捷性。RPSN融合了可微逆运动学算法与神经网络，通过端到端训练，能够以高成功率推测位置。我们将RPSN应用于处理报废电动汽车电池（EOL-EVBs）的移动机械臂上。在多种仿真环境和实体移动机械臂上的大量实验表明，RPSN提供的初始位置为理想位置的概率达96.67%。从运动学约束角度看，该方法平均在1.28次尝试内即可100%生成理想位置，远低于随机采样的31.04次。此外，该方法的样本效率优于纯神经网络方法。所提出的RPSN使机器人能够凭直觉快速推断出可行的目标位置。本工作朝着构建能像人类一样敏捷行动的敏捷机器人迈出了重要一步。