Contact Aware Manipulation Graphs for Kitchen Tool Handling by Domestic Mobile Robots
Keywords:
Mobile Robotics, Manipulation Graphs, Contact Modeling, Kitchen AutomationAbstract
The deployment of mobile robots in domestic environments presents a multitude of complex challenges, particularly when interacting with tools in unstructured spaces such as kitchens. Traditional robotic manipulation techniques often rely on purely kinematic motion planning, which proves inadequate for contact-rich tasks like slicing, stirring, or flipping. This paper introduces a novel framework utilizing Contact-Aware Manipulation Graphs designed specifically for handling kitchen tools by mobile robots. By embedding intricate physical contact constraints, environmental interactions, and tool-specific dynamics directly into a graph-based representation, the proposed system enables robots to reason about forces and transitions simultaneously. The methodology constructs continuous manipulation trajectories by traversing a dynamically generated graph where nodes represent discrete contact states and edges encapsulate the force-dependent transitions between them. Extensive theoretical formulation and descriptive modeling replace classical algebraic constraints, providing a robust foundation for force-compliant planning. Validation through comprehensive simulation and physical experiments demonstrates significant improvements in task success rates, force tracking accuracy, and overall execution efficiency compared to traditional baseline planners. The results indicate that integrating contact awareness into manipulation graphs dramatically enhances a mobile robot's ability to seamlessly adapt to varying kitchen environments, tool geometries, and unexpected dynamic disruptions, paving the way for advanced autonomous domestic assistance.
Downloads
References
1. Cheng, H., Luo, H., Liu, Z., Sun, W., Li, W., & Li, Q. (2025). Reinforcement learning based robust volt/var control in active distribution networks with imprecisely known delay. IEEE Transactions on Computational Social Systems, 1–12.
2. Li, X., Wang, P., Li, G., & Zhang, Y. (2023). Design of a novel self-test-on-chip interface ASIC for capacitive accelerometers. IEEE Transactions on Circuits and Systems I: Regular Papers, 70(7), 2834-2843.
3. Sui, M., Jiang, L., Lyu, T., Wang, H., Zhou, L., Chen, P., & Alhosain, A. (2024). Application of deep learning models based on efficientdet and openpose in user-oriented motion rehabilitation robot control. Journal of Intelligence Technology and Innovation, 2(3), 47-77.
4. Ma, W., Li, Y., Liu, C., Zhang, H., Li, J., Chen, K., & Gao, W. (2026). GeoCraft: A Diffusion Model-Based 3D Reconstruction Method Driven by Image and Point Cloud Fusion. Information Fusion, 104149.
5. Yang, Y., Ma, X., Li, C., Zheng, Z., Zhang, Q., Huang, G., ... & Zhao, Q. (2021). Believe what you see: Implicit constraint approach for offline multi-agent reinforcement learning. Advances in Neural Information Processing Systems, 34, 10299-10312.
6. Wan, H., Zhang, Y., Wang, J., Wu, D., Li, M., Chen, X., ... & Ji, X. (2025). Toward universal embodied planning in scalable heterogeneous field robots collaboration and control. Journal of Field Robotics, 42(5), 2318-2336.
7. Zhang, Luyan. "MCP: A Control-Theoretic Orchestration Framework for Synergistic Efficiency and Interpretability in Multimodal Large Language Models." arXiv preprint arXiv:2509.16597 (2025).
8. Peng, Q., Bai, C., Zhang, G., Xu, B., Liu, X., Zheng, X., ... & Lu, C. (2025, October). NavigScene: Bridging local perception and global navigation for beyond-visual-range autonomous driving. In Proceedings of the 33rd ACM International Conference on Multimedia (pp. 4193-4202).
9. Wang, Siheng, et al. "C3-owd: A curriculum cross-modal contrastive learning framework for open-world detection." arXiv preprint arXiv:2509.23316 (2025).
10. Yang, Y., Wang, Q., Li, C., Hu, H., Wu, C., Jiang, Y., ... & Xu, B. (2025, May). Fewer may be better: Enhancing offline reinforcement learning with reduced dataset. In International Conference on Learning Representations (Vol. 2025, pp. 7076-7098).
11. Wang, W., Dai, H., Dong, C., Cheng, X., Wang, X., Chen, G., & Dou, W. (2019, April). PANDA: Placement of unmanned aerial vehicles achieving 3D directional coverage. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications (pp. 1198-1206). IEEE.
12. Zhang, Y., He, Y., Shao, Y., Yao, Z., Xu, H., Dong, J., ... & Dong, Z. (2026, May). Chromouvqa: Benchmarking vision-language models under chromatic camouflaged images. In ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 12777-12781). IEEE.
13. Tang, A., Hiraoka, T., Hiraoka, N., Shi, F., Kawaharazuka, K., Kojima, K., ... & Inaba, M. (2024, May). Humanmimic: Learning natural locomotion and transitions for humanoid robot via wasserstein adversarial imitation. In 2024 IEEE International Conference on Robotics and Automation (ICRA) (pp. 13107-13114). IEEE.
14. Yang, Z., Ji, W., Guo, Q., & Wang, Z. (2023, October). Javp: Joint-aware video processing with edge-cloud collaboration for dnn inference. In Proceedings of the 31st ACM International Conference on Multimedia (pp. 9152-9160).
15. Li, B., Zhang, R., Liang, H., Zhang, J., Zhang, J., Chen, X., ... & Wang, J. (2026). Interagent: Physics-based multi-agent command execution via diffusion on interaction graphs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 15253-15265).
16. Li, Yuqi, et al. "Ammkd: Adaptive multimodal multi-teacher distillation for lightweight vision-language models." arXiv preprint arXiv:2509.00039 (2025).
17. Wang, H., Li, Z., Zhang, Y., He, Z., Jiang, L., Li, K., ... & Gu, D. (2026). ConsistNav: Closing the Action Consistency Gap in Zero-Shot Object Navigation with Semantic Executive Control. arXiv preprint arXiv:2605.09869.
18. Li, Q., Luo, H., Cheng, H., Deng, Y., Sun, W., Li, W., & Liu, Z. (2023). Incipient fault detection in power distribution system: A time-frequency embedded deep-learning-based approach. IEEE Transactions on Instrumentation and Measurement, 72, 1–14.
19. Yang, Z., Liang, B., & Ji, W. (2021). An intelligent end–edge–cloud architecture for visual IoT-assisted healthcare systems. IEEE internet of things journal, 8(23), 16779-16786.
20. Mur-Artal, R., & Tardós, J. D. (2017). ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Transactions on Robotics, 33(5), 1255–1262.
21. Wang, Y., Lou, M., Liang, W., & Zhang, C. (2023). Numerical and experimental investigation on tensile fatigue performance of reinforced thermoplastic pipes. Ocean Engineering, 287, 115814.
22. Qu, W., Wang, J., Gong, Y., Huang, X., & Xiao, L. (2025). An end-to-end robust point cloud semantic segmentation network with single-step conditional diffusion models. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 27325-27335).
23. Wan, H., Chen, Y., Deng, Y., Wei, Z., Li, D., Lin, Z., ... & Ji, X. (2025, October). EmbodiedAgent: A Scalable Hierarchical Approach to Overcome Practical Challenge in Multi-Robot Control. In 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 12140-12146). IEEE.
24. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the Inception architecture for computer vision. In Proceedings of CVPR.
25. Yang, Y., Hu, H., Li, W., Li, S., Yang, J., Zhao, Q., & Zhang, C. (2023, June). Flow to control: Offline reinforcement learning with lossless primitive discovery. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 9, pp. 10843-10851).
26. Luo, H., Feng, Q., Sun, W., Li, W., Li, Q., Zhao, W., & Liu, Z. (2026). FD-Mamba with neural observer and frequency-enhanced update for incipient feeder fault detection. IEEE Internet of Things Journal, 13(10), 20761–20772.
27. Hutchinson, S., Hager, G. D., & Corke, P. I. (1996). A tutorial on visual servo control. IEEE Transactions on Robotics and Automation, 12(5), 651–670.
28. Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of CVPR.
29. Qu, W., Shao, Y., Meng, L., Huang, X., & Xiao, L. (2024). A conditional denoising diffusion probabilistic model for point cloud upsampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 20786-20795).
30. Kamal, T., Wenhong, T., Guo, J., Shafiq, M., Shuaihong, J., & Xue, R. (2026). Adaptive Probabilistic Transformer for Medical Image Segmentation. Expert Systems with Applications, 131948.
31. Yang, Z., Ji, W., & Wang, Z. (2024). Adaptive joint configuration optimization for collaborative inference in edge-cloud systems. Science China Information Sciences, 67(4), 149103.
32. Yu, D., Mahamadu, A. M., Chen, W., & Li, N. (2026). Understanding the challenges and future trends of immersive technology use in indoor pedestrian and evacuation dynamics: A systematic review. Journal of Building Engineering, 115703.
33. Wang, C., Ke, L., Liu, X., Huang, D., Yao, J., & Chu, W. Explainable AI Analysis of Brake Control in CARLA Through Reference and Distilled Policies. Frontiers in Robotics and AI, 13, 1847808.
34. Xiao, Q., & Ghaffari, M. (2026). LaWM: Least Action World Models for Long-Horizon Physical Consistency from Visual Observations. arXiv preprint arXiv:2605.08279.
35. Yang, Z., Ji, W., Guo, Q., Zhao, J., Zhao, C., Zhang, X., ... & You, Y. (2026). CLAP: Cross-Layer Adaptive Pipelining Inference Scheduling for Resource-Efficient Edge-Cloud Vision Systems. ACM Transactions on Architecture and Code Optimization.
36. Wan, H., Cheng, J., Deng, Y., Wu, D., Chen, Y., Lin, Z., ... & Ji, X. Towards Physics Aware Embodied Control with Graph based Object-centric Learning. ACM Transactions on Cyber-Physical Systems.
37. Lv, Qi, et al. "Spatial-temporal graph diffusion policy with kinematic modeling for bimanual robotic manipulation." Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.
38. Hu, Y., Xi, A., Xiao, Q., Isaacson, S., Liu, H. X., Vasudevan, R., & Ghaffari, M. (2026). LongNav-R1: Horizon-Adaptive Multi-Turn RL for Long-Horizon VLA Navigation. arXiv preprint arXiv:2602.12351.
39. Wang, Y., Fang, Y., Wang, T., Li, L., Liu, K., Xu, J., ... & Xu, R. (2026). What Limits Vision-and-Language Navigation?. arXiv preprint arXiv:2605.13328.
40. Liu, Y., & Kwon, H. (2025). Efficient Depth Estimation for Unstable Stereo Camera Systems on AR Glasses. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6252-6261).
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Florence Morel
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is licensed under the Creative Commons Attribution 4.0 International License. This license permits use, sharing, and adaptation of the article for non-commercial purposes, provided the original authors and source are properly credited.
Alternatively, you may choose other Creative Commons licenses based on your preferences:
-
CC Attribution-NonCommercial-NoDerivatives 4.0: This license allows others to download the works and share them with others as long as they credit the author, but they can’t change them in any way or use them commercially.
-
CC Attribution-NonCommercial 4.0: This license allows others to remix, tweak, and build upon the work non-commercially, as long as they credit the author, but they cannot use the material for commercial purposes.
-
CC Attribution-NonCommercial-ShareAlike 4.0: This allows others to remix, tweak, and build upon the work non-commercially, as long as they credit the author and license their new creations under identical terms.
These terms ensure that your article is freely accessible and usable, while respecting the rights of the original authors.
