用机器人运动学知识提升神经网络采样效率，加速任务规划。
Decoding RobKiNet: Insights into Efficient Training of Robotic Kinematics Informed Neural Network
- 将运动学知识注入神经网络，实现端到端配置空间采样。
- 训练速度比深度强化学习快74.29倍，采样准确率达99.25%。
- 适合需要高效任务规划的机器人系统，如移动操作臂。
在机器人任务与运动规划（TAMP）中，从机器人配置空间中采样以满足任务级全局约束并提升后续运动规划效率至关重要。由于多层级约束下关节配置采样的复杂性，传统方法常效率低下。本文提出RobKiNet原理，一种基于运动学信息的神经网络，用于在配置空间多约束条件下实现连续可行集（CFS）的端到端采样，并建立其优化期望模型。与传统采样和学习型方法对比表明，RobKiNet通过融合运动学知识显著提升训练效率，确保梯度优化稳定且准确。2-自由度空间中的可视化与定量分析验证了其理论效率；在9自由度自主移动操作臂（AMMR）上的应用显示其具备优异的整体与解耦控制能力，在电池拆卸任务中表现突出。相较于深度强化学习，其训练速度提升74.29倍，采样准确率高达99.25%，在真实场景中实现97.33%的任务完成率。
原文摘要 · Abstract (English)
In robots task and motion planning (TAMP), it is crucial to sample within the robot's configuration space to meet task-level global constraints and enhance the efficiency of subsequent motion planning. Due to the complexity of joint configuration sampling under multi-level constraints, traditional methods often lack efficiency. This paper introduces the principle of RobKiNet, a kinematics-informed neural network, for end-to-end sampling within the Continuous Feasible Set (CFS) under multiple constraints in configuration space, establishing its Optimization Expectation Model. Comparisons with traditional sampling and learning-based approaches reveal that RobKiNet's kinematic knowledge infusion enhances training efficiency by ensuring stable and accurate gradient optimization.Visualizations and quantitative analyses in a 2-DOF space validate its theoretical efficiency, while its application on a 9-DOF autonomous mobile manipulator robot(AMMR) demonstrates superior whole-body and decoupled control, excelling in battery disassembly tasks. RobKiNet outperforms deep reinforcement learning with a training speed 74.29 times faster and a sampling accuracy of up to 99.25%, achieving a 97.33% task completion rate in real-world scenarios.
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