  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.
