Decoding RobKiNet: Insights into Efficient Training of Robotic Kinematics Informed Neural Network

2025-09-09

AI Summary

In robot task and motion planning (TAMP), low sampling efficiency and poor feasibility guarantees in configuration space under multi-level constraints remain challenging. To address this, we propose RobKiNet: an end-to-end neural sampling model that explicitly incorporates analytical kinematic priors. Its key innovation lies in embedding closed-form kinematic constraints directly into the gradient-based optimization of a differentiable expectation optimization framework, enabling stable and efficient learning of continuous feasible sets. The model supports both whole-body coordination and decoupled control, balancing generalization and real-time performance. Rigorous convergence and feasibility are validated on a 2-DOF theoretical benchmark. On a 9-DOF mobile manipulation platform, RobKiNet achieves 99.25% sampling accuracy, accelerates training by 74.29× over baselines, and attains a 97.33% task success rate in real-world scenarios.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Efficient sampling in robot configuration space under constraints
• Training neural networks with kinematic knowledge for gradient optimization
• Achieving high accuracy and speed in robotic task planning

Innovation

• Kinematics-informed neural network for constraint-based sampling
• End-to-end sampling within continuous feasible configuration space
• Enhanced training efficiency via stable gradient optimization