# RobKiNet: Robotic Kinematics Informed Neural Network for Optimal Robot Configuration Prediction

arXiv: https://arxiv.org/abs/2402.16281 · HTML: https://mlsanity.com/paper/2402.16281/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 58 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Builds on (most cited first, 23 of 23)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [Integrated Task and Motion Planning](https://mlsanity.com/paper/2010.01083/) (2020)
- [PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning](https://mlsanity.com/paper/1802.08705/) (2018)
- [Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning](https://mlsanity.com/paper/2304.13653/) (2023)
- [A Differentiable Physics Engine for Deep Learning in Robotics](https://mlsanity.com/paper/1611.01652/) (2016)
- [CAN-PINN: A Fast Physics-Informed Neural Network Based on Coupled-Automatic-Numerical Differentiation Method](https://mlsanity.com/paper/2110.15832/) (2021)
- [A Differentiable Programming System to Bridge Machine Learning and Scientific Computing](https://mlsanity.com/paper/1907.07587/) (2019)
- [Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners](https://mlsanity.com/paper/1907.06013/) (2019)
- [Learning compositional models of robot skills for task and motion planning](https://mlsanity.com/paper/2006.06444/) (2020)
- [FFRob: Leveraging Symbolic Planning for Efficient Task and Motion Planning](https://mlsanity.com/paper/1608.01335/) (2016)
- [Hierarchical Object-to-Zone Graph for Object Navigation](https://mlsanity.com/paper/2109.02066/) (2021)
- [Learning to guide task and motion planning using score-space representation](https://mlsanity.com/paper/1807.09962/) (2018)
- [Learning Kinematic Feasibility for Mobile Manipulation through Deep Reinforcement Learning](https://mlsanity.com/paper/2101.05325/) (2021)
- [Sequence-Based Plan Feasibility Prediction for Efficient Task and Motion Planning](https://mlsanity.com/paper/2211.01576/) (2022)
- [Whole-Body MPC for a Dynamically Stable Mobile Manipulator](https://mlsanity.com/paper/1902.10415/) (2019)
- [Dynamically Conservative Self-Driving Planner for Long-Tail Cases](https://mlsanity.com/paper/2305.07497/) (2023)
- [Learning to Search in Task and Motion Planning with Streams](https://mlsanity.com/paper/2111.13144/) (2021)
- [MaestROB: A Robotics Framework for Integrated Orchestration of Low-Level Control and High-Level Reasoning](https://mlsanity.com/paper/1806.00802/) (2018)
- [Differentiable Programming: Efficient Smoothing of Control-Flow-Induced Discontinuities](https://mlsanity.com/paper/2305.06692/) (2023)
- [Autonomous Electric Vehicle Battery Disassembly Based on NeuroSymbolic Computing](https://mlsanity.com/paper/2205.07441/) (2022)

## Built on by (most cited first, 1 of 1)

- [Revolutionizing Battery Disassembly: The Design and Implementation of a Battery Disassembly Autonomous Mobile Manipulator Robot(BEAM-1)](https://mlsanity.com/paper/2407.06590/) (2024)

## Abstract (arXiv)

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.