Roadmap for robotics AI

Machine Learning


  • Krizhevsky, A., Sutskever, I. & Hinton, Deep Convolutional Neural Network with Ge Imagenet Classification. commune. ACM 6084–90 (2017).

    ArticleGoogle Scholar

  • Siciliano, B. & Hatib, O. Robot Handbook (Springer, 2016).

  • Siciliano, B., Sciavicco, L., Villani, L. & Oriolo, G. Robotics: Modeling, Planning, Control (Springer, 2009).

  • Siegwart, R., Nourbakhsh, Ir & Scaramuzza, D. Introducing Autonomous Mobile Robots (MIT Press, 2011).

  • Billard, A. & Kragic, D. Trends and challenges in robot operation. Science 364EAAT8414 (2019).

    ArticleGoogle Scholar

  • Ravichandar, H. Etal. Recent advances in robots that learn from demonstrations. Anne. Rev. Control robot. Orton. syst. 3297–330 (2020).

    ArticleGoogle Scholar

  • Mahler, J. Etal. Understand the policy to understand ambidextrous robots. SCI. robot. J. 4EAAU4984 (2019).

    ArticleGoogle Scholar

  • Kim, S., Shukla, A. & Billard, A. Catching objects in flight. IEEE Transformer. robot. 301049–1065 (2014).

    ArticleGoogle Scholar

  • Loquercio, A. Etal. Learn to fly in the wild. SCI. robot. 6EABG5810 (2021).

    ArticleGoogle Scholar

  • Grollman, DH & Billard, A. Donut like me: learn from failed demonstrations. in Proc. IEEE International Conference on Robotics and Automation (ICRA) 3804–3809 (IEEE, 2011).

  • Chen, L., Paleja, R. & Gombolay, M. in Proc. 2020 Conference on Robot Learning (eds Kober, J. et al.) 1262–1277 (PMLR, 2021).

  • Butepage, J., Black, M.J., Kragic, D. & Kjellstrom, H. Deep expression learning for prediction and classification of human movement. in Proc. 2017 IEEE Computer Vision and Pattern Recognition Conference (CVPR)) 1591–1599 (IEEE, 2017).

  • Calinon, S. & Billard, A. Proactive education in robot programming through demonstrations. in Proc. Ro -Man 2007-16th IEEE International Symposium on Interactive Communication between Robots and Humans 702–707 (IEEE, 2007).

  • Zollner, R., Asfour, T. & Dillmann, R. Programming with demo: Dual arm manipulation tasks for humanoid robots. in Proc. 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IRO) 479–484 (IEEE, 2004).

  • Nicolescu, M. &Mataric, MJ in Imitation and social learning in robots, humans and animals (eds Nehaniv, Cl & Dautenhahn, K.) 407–424 (Cambridge Univ. Press, 2005).

  • Florence, P. Etal. Implicit behavior cloning. in Proc. 5th meeting on robot learning (Eds Faust, A. et al.) 158–168 (PMLR, 2022).

  • Sutton, RS & Barto, AG Reinforcement Learning: Introduction (MIT Press, 1998).

  • Kaufman, E. et al. Champion-level drone race using deep reinforcement learning. Nature 620982–987 (2023).

    ArticleGoogle Scholar

  • Radosavovic, I. Etal. A real-world humanoid movement through reinforced learning. SCI. robot. 9EADI9579 (2024).

    ArticleGoogle Scholar

  • Haarnoja, T. Etal. Learning agile soccer skills for bipedal robots with deep reinforcement learning. SCI. robot. 9EADI8022 (2024).

    ArticleGoogle Scholar

  • Ibarz, J. Etal. How to Train Your Robots with Deep Reinforcement Learning: Lessons Learned. int. J. Robot. res. 40698–721 (2021).

    ArticleGoogle Scholar

  • Kober, J. & Peters, J. Imitation and reinforcement learning. IEEE robot. Automatic. magazine. 1755–62 (2010).

    ArticleGoogle Scholar

  • Hester, T. Etal. Deep Q learning from demonstrations. in Proc. 32 AAAI Conferences on Artificial Intelligence and Innovative Applications of Artificial Intelligence and 8th AAAI Symposium 8th AAAI Symposium on Educational Advancements in Artificial Intelligence (eds McIlraith, SA & Weinberger, KQ) 3223–3230 (AAAI Press, 2018).

  • Adams, S., Tyler, C. & Belling, PA reverse reinforcement learning investigation. artif. Intel. Pastor 554307–4346 (2022).

    ArticleGoogle Scholar

  • Kim, D. A review of machine learning methods for other software robotics. PLOS 1 16E0246102 (2021).

    ArticleGoogle Scholar

  • Subramanian, S. Etal. Learn human signatures using scalable tactile gloves. Nature 569698–702 (2019).

    ArticleGoogle Scholar

  • Mahler, J. & Goldberg, K. By simulating a deep policy learning robust grasp sequence of robot bin picking. in Proc. First Annual Meeting on Robot Learning (eds Levine, S. et al.) 515–524 (PMLR, 2017).

  • O'Neill, A. Etal. Open X-Embodiment: robot learning dataset and RT-X models. in Proc. 2024 International Conference on IEEE Robotics and Automation (ICRA) 6892–6903 (IEEE, 2024).

  • Qi, H., Kumar, A., Calandra, R., Ma, Y. & Malik, J. in Proc. 6th meeting on robot learning (Eds Liu, K. et al.) 1722–1732 (PMLR, 2023).

  • Khandate, G. Etal. Sampling-based exploration for reinforcement learning of dexterous operations. in Proc. Robotics: Science and Systems XIX (eds Bekris, K. et al.) (RSS, 2023).

  • Chen, T. Etal. Visual dexterity: A novel and complex object shape redirects the hands. SCI. robot. 8EADC9244 (2023).

    ArticleGoogle Scholar

  • Kumar, A., Fu, Z., Pathak, D. & Malik, J. RMA: Rapid motion adaptation of foot robots. in Proc. Robotics: Science and Systems XVII (Eds Shell, Da et al.) (RSS, 2021).

  • Vaswani, A. Etal. Care is required. in Proc. Advances in neural information processing systems 30 (eds Guyon, I. et al.) (Nips, 2017).

  • Shah, D. Etal. Navigation using large-scale language models: Semantic gas work as a heuristic for planning. in Proc. 7th meeting on robot learning (Eds Tan, J. etal.) 2683–2699 (PMLR, 2023).

  • Gen, Z. Etal. Pre-training in vision languages: basics, recent advances, future trends. Computer Graphics and Vision Fundamentals and Trends Vol. 14 (current publisher, 2022).

  • Brohan A. et al. RT-2: Vision-Language-active model transfers web knowledge to robot controls. in Proc. 7th meeting on robot learning (Eds Tan, J. etal.) 2165–2183 (PMLR, 2023).

  • Toussaint, M. Logical Geometric Programming: An optimization-based approach that combines tasks and motion planning. in Proc. 24th International Joint Meeting on Artificial Intelligence (Eds Yang, Q. & Wooldridge, M.) 1930–1936 (AAAI Press, International Joint Meeting, Artificial Intelligence, 2015).

  • Semeraro, F. Etal. Human-Robot Collaboration and Machine Learning: A Systematic Review of Recent Research. Rob. computer. Integration. Manf. 79102432 (2023).

    ArticleGoogle Scholar

  • Brunke, L. Etal. Safe learning in robotics: from learning-based control to safe reinforcement learning. Anne. Rev. Control robot. Orton. syst. 5411–444 (2022).

    ArticleGoogle Scholar

  • Brunke, L. Etal. Learning-based model predictive control: Towards safe learning in control. Anne. Rev. Control robot. Orton. syst. 3269–296 (2020).

    ArticleGoogle Scholar

  • Nghiem, Tx et al. Physics-based machine learning for modeling and control of dynamic systems. in Proc. 2023 American Control Conference (ACC) 3735–3750 (IEEE, 2023).

  • Tsunami, H. , Chung, S.-J. & Slotin, J.-JE contraction theory for nonlinear stability analysis and learning-based control: a tutorial overview. Anne. Rev. Control 52135–169 (2021).

    ArticleMathscinet Google Scholar

  • Kang, D., Cheng, J., Zamora, M., Zargarbashi, F. & Coros, S. RL+Model-Based Control: Learn versatile leg movement using on-demand optimal control. IEEE robot. Automatic. Rhett. 86619–6626 (2023).

    ArticleGoogle Scholar

  • Ichnowski, J. Etal. Deep learning allows you to acquire grasped motion planning. SCI. robot. 5EABD7710 (2020).

    ArticleGoogle Scholar

  • Thrun, S. & Mitchell, TM Lifelong Robot Learning. Rob. Automatic. syst. 1525–46 (1995).

    ArticleGoogle Scholar

  • Aliasghari, P., Ghafurian, M., Nehaniv, Cl & Dautenhahn, K. int. J. Soc. robot. 162079–2105 (2024).

    ArticleGoogle Scholar

  • Dahiya, R., Akinwande, D. & Chang, JS Flexible Electronic Skin: From Humanoids to Humans. Proc. IEEE 1072011–2015 (2019).

    ArticleGoogle Scholar



  • Source link

    Leave a Reply

    Your email address will not be published. Required fields are marked *