Task and Motion Planning (TAMP) is essential for robots to interact with the world and accomplish complex tasks. The TAMP problem involves a critical gap: exploring the robot's configuration parameters (such as chassis position and robotic arm joint angles) within continuous space to ensure that task-level global constraints are met while also enhancing the efficiency of subsequent motion planning. Existing methods still have significant room for improvement in terms of efficiency. Recognizing that robot kinematics is a key factor in motion planning, we propose a framework called the Robotic Kinematics Informed Neural Network (RobKiNet) as a bridge between task and motion layers. RobKiNet integrates kinematic knowledge into neural networks to train models capable of efficient configuration prediction. We designed a Chassis Motion Predictor(CMP) and a Full Motion Predictor(FMP) using RobKiNet, which employed two entirely different sets of forward and inverse kinematics constraints to achieve loosely coupled control and whole-body control, respectively. Experiments demonstrate that CMP and FMP can predict configuration parameters with 96.67% and 98% accuracy, respectively. That means that the corresponding motion planning can achieve a speedup of 24.24x and 153x compared to random sampling. Furthermore, RobKiNet demonstrates remarkable data efficiency. CMP only requires 1/71 and FMP only requires 1/15052 of the training data for the same prediction accuracy compared to other deep learning methods. These results demonstrate the great potential of RoboKiNet in robot applications.

翻译：任务与运动规划（TAMP）对于机器人实现与世界的交互并完成复杂任务至关重要。TAMP问题存在一个关键挑战：需要在连续空间中探索机器人的构型参数（例如底盘位置和机械臂关节角度），以确保满足任务层面的全局约束，同时提升后续运动规划的效能。现有方法在效率方面仍有较大提升空间。认识到机器人运动学是运动规划的关键因素，我们提出一个名为机器人运动学信息神经网络（RobKiNet）的框架，作为任务层与运动层之间的桥梁。RobKiNet将运动学知识整合到神经网络中，以训练能够高效预测构型的模型。我们利用RobKiNet设计了一个底盘运动预测器（CMP）和一个全身运动预测器（FMP），它们分别采用两套完全不同的正逆运动学约束来实现松耦合控制和全身控制。实验表明，CMP和FMP分别能以96.67%和98%的准确率预测构型参数。这意味着相应的运动规划相较于随机采样方法，可分别实现24.24倍和153倍的加速。此外，RobKiNet展现出卓越的数据效率。在达到相同预测精度的情况下，CMP仅需其他深度学习方法1/71的训练数据，而FMP仅需1/15052的训练数据。这些结果证明了RoboKiNet在机器人应用中的巨大潜力。