A Theoretical Review of Robotic Manipulation: Approach and Grasp
DOI:
https://doi.org/10.69996/ijari.2026007Keywords:
Deep Learning, Object Approaching, Robotic Grasping, Tactile Sensing, Vision-Based ControlAbstract
Robotic object approaching and grasping remain fundamental challenges in the field of autonomous manipulation, especially in unstructured and dynamic environments. Recent developments in sensing technologies, control strategies, and learning-based algorithms have improved grasp reliability and adaptability to a large extent. This paper presents a technical overview of modern robotic grasping systems with a focus on vision-based, tactile, and visuo-tactile sensing frameworks, along with deep learning and reinforcement learning methods for grasp synthesis and control. Instead of extensive theoretical discussion, this review focuses on system architectures, mathematical formulations, and algorithmic pipelines used in contemporary research. Key performance metrics such as grasp success rate, robustness to uncertainty, and computational efficiency are examined. The comparative analysisof the various methods gives strengths, limitations, and suitability for real-world applications such as warehouse automation and assistive robotics. The paper aims to provide a concise yet technically rich reference for researchers and practitioners working in the field of robotic manipulation.
References
[1] G. J. Garcia, J. A. Corrales, J. Pomares and F. Torres, “Survey of visual and force/tactile control of robots for physical interaction in Spain,” Sensors, 9(12), 2009, 9689–9733.
[2] C. Chi, X. Sun, N. Xue, T. Li and C. Liu, “Recent progress in technologies for tactile sensors,” Sensors, 18(4), 2018, 32–40.
[3] L. Zou, C. Ge, Z. J. Wang, E. Cretu and X. Li, “Novel tactile sensor technology and smart tactile sensing systems: A review,” Sensors, 17(11), 2017, 1–24.
[4] S. Hutchinson, G. D. Hager and P. I. Corke, “A tutorial on visual servo control,” IEEE Transactions on Robotics and Automation, 12(5), 1996, 651–670.
[5] B. Columbia, “Haptic perception: A tutorial,” Attention, Perception, & Psychophysics, 71(7), 2009, 1233–1240, 2009.
[6] S. Luo, J. Bimbo, R. Dahiya and H. Liu, “Robotic tactile perception of object properties: A review,” Mechatronics, 48, 2017, 54–67.
[7] D. Fischinger, M. Vincze and Y. Jiang, “Learning grasps for unknown objects in cluttered scenes,” in Proc. IEEE Int. Conf. Robotics and Automation (ICRA), 2013, 609– 616.
[8] M. Gualtieri, A. ten Pas, K. Saenko and R. Platt, “High precision grasp pose detection in dense clutter,” in Proc. IEEE Int. Conf. Intelligent Robots and Systems (IROS), 2016, 598–605.
[9] S. Panda, A. H. A. Hafez and C. V. Jawahar, “Learning support order for manipulation in clutter,” in Proc. IEEE Int. Conf. Intelligent Robots and Systems (IROS), 2013, 809–815.
[10] S. Panda, A. H. Abdul Hafez and C. V. Jawahar, “Single- and multiple-view support order prediction in clutter for manipulation,” Journal of Intelligent & Robotic Systems, 83(2), 2016, 179–203.
[11] R. Mojtahedzadeh, A. Bouguerra, E. Schaffernicht and A. J. Lilienthal, “Support relation analysis and decision making for safe robotic manipulation tasks,” Robotics and Autonomous Systems, 71, 2015, 99–117.
[12] J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks, 61, 2015, 85–117.
[13] M. A. Nielsen, Neural Networks and Deep Learning. San Francisco, CA, USA: Determination Press, 2015.
[14] T. Zhao, M. Deng, Z. Li and Y. Hu, “Cooperative manipulation for a mobile dual-arm robot using sequences of dynamic movement primitives,” IEEE Transactions on Cognitive and Developmental Systems, 12(1), 2018,18–29.
[15] S. Krishnan, A. Garg, R. Liaw, B. Thananjeyan, L. Miller, F. T. Pokorny and K. Goldberg, “SWIRL: A sequential windowed inverse reinforcement learning algorithm for robot tasks with delayed rewards,” International Journal of Robotics Research, 38(2–3), 2019, 126–145.
[16] V. François-Lavet, P. Henderson, R. Islam, M. G. Bellemare and J. Pineau, “An introduction to deep reinforcement learning,” Foundations and Trends in Machine Learning, 11(3–4), 2018, 15–23.
[17] T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver and D. Wierstra, “Continuous control with deep reinforcement learning,” arXiv preprint arXiv:1509.02971, 2015.
[18] N. Heess, S. Sriram, J. Lemmon, J. Merel, G. Wayne, Y. Tassa, T. Erez, Z. Wang, S. M. A. Eslami, M. Riedmiller and D. Silver, “Emergence of locomotion behaviours in rich environments,” arXiv preprint arXiv:1707.02286, 2017.
[19] J. Schulman, S. Levine, P. Abbeel, M. Jordan and P. Moritz, “Trust region policy optimization,” in Proc. 32nd Int. Conf. Machine Learning (ICML), 2015, 1889– 1897.
[20] V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Harley, T. P. Lillicrap, D. Silver and K. Kavukcuoglu, “Asynchronous methods for deep reinforcement learning,” in Proc. Int. Conf. Machine Learning (ICML), 2016, 1928–1937.
[21] R. V. Martinez, J. L. Branch, C. R. Fish, L. Jin, R. F. Shepherd, R. M. D. Nunes, Z. Suo and G. M. Whitesides, “Robotic tentacles with three-dimensional mobility based on flexible elastomers,” Advanced Materials, 25(2), 2012, 205–212.
[22] A. A. Stokes, R. F. Shepherd, S. A. Morin, F. Ilievski and G. M. Whitesides, “A hybrid combining hard and soft robots,” Soft Robotics, 1(1), 2014, 70–74.
[23] M. Q. Mohammed, K. L. Chung and C. S. Chyi, “Pick and place objects in a cluttered scene using deep reinforcement learning,” International Journal of Mechanical and Mechatronics Engineering, 20(4), 2020, 50–57.
[24] J. Amend, N. Cheng, S. Fakhouri and B. Culley, “Soft robotics commercialization: Jamming grippers from research to product,” Soft Robotics, 3(4), 2016, 213–222.
[25] M. Ciocarlie, F. M. Hicks, R. Holmberg, J. Hawke, M. Schlicht, J. Gee, S. Stanford and R. Bahadur, “The Velogripper: A versatile single-actuator design for enveloping,” International Journal of Robotics Research, 33(5), 2014, 753–767.
[26] F. Lévesque, B. Sauvet, P. Cardou and C. Gosselin, “A model-based scooping grasp for the autonomous picking of unknown objects with a two-fingered gripper,” Robotics and Autonomous Systems, 106, 2018, 14–25.
[27] V. Babin, D. St-Onge and C. Gosselin, “Stable and repeatable grasping of flat objects on hard surfaces using passive and epicyclic mechanisms,” Robotics and Computer-Integrated Manufacturing, 55, 2019,1–10.
[28] J. Amend, N. Cheng, S. Fakhouri and B. Culley, “Soft robotics commercialization: Jamming grippers from research to product,” Soft Robotics, 3(4), 2016, p. 213– 222.
[29] V. Babin and C. Gosselin, “Picking, grasping, or scooping small objects lying on flat surfaces: A design approach,” International Journal of Robotics Research, 37(12), 2018, 1484–1499.
[30] Y. Chen, S. Guo, C. Li, H. Yang and L. Hao, “Size recognition and adaptive grasping using an integration of actuating and sensing soft pneumatic gripper,” Robotics and Autonomous Systems, 104, 2018, 14–24.
[31] I. Sarantopoulos and Z. Doulgeri, “Human-inspired robotic grasping of flat objects,” Robotics and Autonomous Systems, 108, 2018, 179–191.
[32] R. Deimel, “A novel type of compliant and underactuated robotic hand for dexterous grasping,” International Journal of Robotics Research, 35(3), 2016, 161–185.
[33] W. Crooks, C. Rogers, M. O’Sullivan, G. Vukasin and W. Messner, “Fin Ray effect inspired soft robotic gripper: From the RoboSoft grand challenge towards optimization,” Frontiers in Robotics and AI, 3, 2016, 1– 9.
[34] W. Crooks, S. Rozen-Levy, B. Trimmer, C. Rogers and W. Messner, “Passive gripper inspired by Manduca sexta and the Fin Ray effect,” International Journal of Advanced Robotic Systems, 14(4), 2017, 1–7.
[35] J. M. Gandarias, J. M. Gómez-de-Gabriel and A. J. García-Cerezo, “Enhancing perception with tactile object recognition in adaptive grippers for human-robot interaction,” Sensors, 18(3), 2018, 1–20.
[36] J. Nassour, V. Ghadiya, V. Hugel and F. H. Hamker, “Design of a new sensory soft hand: Combining air-pump actuation with superimposed curvature and pressure sensors,” in Proc. IEEE Int. Conf. Soft Robotics (RoboSoft), 2018, 164–169.
[37] J. Zhang, A. Jackson, N. Mentzer and R. Kramer, “A modular, reconfigurable mold for a soft robotic gripper design activity,” Frontiers in Robotics and AI, 4, 2017 pp. 1–8.
[38] S. A. Suresh, P. Glick, D. Ruffatto, M. T. Tolley, M. Cutkosky and A. Parness, “A soft robotic gripper with gecko-inspired adhesive,” IEEE Robotics and Automation Letters, 3(2), 2028, 903–910.
[39] S. Terryn, J. Brancart, D. Lefeber, G. Van Assche and B. Vanderborght, “Self-healing soft pneumatic robots,” Science Robotics, 2(9), 2017, 1–13.
[40] J. P. Roberge, W. Ruotolo, V. Duchaine and M. Cutkosky, “Improving industrial grippers with adhesion-controlled friction,” IEEE Robotics and Automation Letters, 3(2), 2018, 1041–1048.
[41] R. Bemelmans, G. J. Gelderblom, P. Jonker and L. de Witte, “Socially assistive robots in elderly care: A systematic review into effects and effectiveness,” Journal of the American Medical Directors Association, 13(2), 2012, 114–120.
[42] M. R. Maymó, A. Shafti and A. A. Faisal, “Fast Orient: Lightweight computer vision for wrist control in assistive robotic grasping,” arXiv preprint arXiv:1807.08275, 2018.
[43] H. Al-Fahaam, S. Davis, and S. Nefti-Meziani, “The design and mathematical modelling of novel extensor bending pneumatic artificial muscles (EBPAMs) for soft exoskeletons,” Robotics and Autonomous Systems, 99, 2018, 63–74.
[44] J. Yi, X. Chen and Z. Wang, “A three-dimensional-printed soft robotic glove with enhanced ergonomics and force capability,” IEEE Robotics and Automation Letters, 3(1), 2017, 242–248.
[45] G. Grioli, A. Bicchi, F. Felici, M. G. Catalano, A. S. Ciullo and A. Ajoudani, “Analytical and experimental analysis for position optimisation of a grasp assistance supernumerary robotic hand,” IEEE Robotics and Automation Letters, 3( 4), 2018, 4305–4312.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Advance Research and Innovation(IJARI, ISSN: 2347-3258)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Fringe Global Scientific Press publishes all the papers under a Creative Commons Attribution-Non-Commercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/) license. Authors have the liberty to replicate and distribute their work. Authors have the ability to use either the whole or a portion of their piece in compilations or other publications that include their own work. Please see the licensing terms for more information on reusing the work.
