Resources

Some good resources and papers to take a look if you are applying for PhD, Research Assistant or Research Intern positions:

具身智能技术指南 Embodied-AI-Guide

How to Get Started with Embodied AI Research

Keynote Talks in ICRA/IROS

CoRL 2025 Livestream

RSS 2025 Livestream

Lumina具身智能社区

Robot Learning (Embodied AI)

1. Zhao, T., Kumar, V., Levine, S., & Finn, C. (2023). Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware. Robotics: Science and Systems (RSS 2023).

2. Chi, C., Xu, Z., Feng, S., Cousineau, E., Du, Y., Burchfiel, B., ... & Song, S. (2023). Diffusion policy: Visuomotor policy learning via action diffusion. The International Journal of Robotics Research, 02783649241273668.

3. Luo, J., Xu, C., Wu, J., & Levine, S. (2025). Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning. Science Robotics. 10, eads5033. DOI:10.1126/scirobotics.ads5033

4. Lei, K., Li, H., Yu, D., Wei, Z., Guo, L., Jiang, Z., ... & Xu, H. (2025). RL-100: Performant Robotic Manipulation with Real-World Reinforcement Learning. arXiv preprint arXiv:2510.14830.

5. Li, H., Lei, K., Zang, S., Hu, K., Liang, Y., An, B., ... & Xu, H. (2026). Failure-Aware RL: Reliable Offline-to-Online Reinforcement Learning with Self-Recovery for Real-World Manipulation. arXiv preprint arXiv:2601.07821.

6. Zhao, Y., Jin, H., Jiang, L., Zhang, X., Wu, K., Ren, P., ... & Tang, J. (2025). Real-world Reinforcement Learning from Suboptimal Interventions. arXiv preprint arXiv:2512.24288.

7. Intelligence, P., Amin, A., Aniceto, R., Balakrishna, A., Black, K., Conley, K., ... & Zhou, Z. (2025). $\pi^{*} _ {0.6} $: a VLA That Learns From Experience. arXiv preprint arXiv:2511.14759.

8. Pan, M., Feng, S., Zhang, Q., Li, X., Song, J., Qu, C., ... & Luo, J. (2026). SOP: A Scalable Online Post-Training System for Vision-Language-Action Models. arXiv preprint arXiv:2601.03044.

9. Li, Y., Ma, X., Xu, J., Cui, Y., Cui, Z., Han, Z., ... & Wu, Y. (2025). GR-RL: Going dexterous and precise for long-horizon robotic manipulation. arXiv preprint arXiv:2512.01801.

10. Hou, Y., Liu, Z., Chi, C., Cousineau, E., Kuppuswamy, N., Feng, S., ... & Song, S. (2024). Adaptive Compliance Policy: Learning Approximate Compliance for Diffusion Guided Control. arXiv preprint arXiv:2410.09309.

11. Choi, H., Hou, Y., Pan, C., Hong, S., Patel, A., Xu, X., ... & Song, S. (2026). In-the-Wild Compliant Manipulation with UMI-FT. arXiv preprint arXiv:2601.09988.

12. Xue, H., Ren, J., Chen, W., Zhang, G., Fang, Y., Gu, G., ... & Lu, C. (2025). Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation. arXiv preprint arXiv:2503.02881.

13. Chen, W., Xue, H., Wang, Y., Zhou, F., Lv, J., Jin, Y., ... & Lu, C. (2025). ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning. arXiv preprint arXiv:2512.10946.

14. Yu, J., Liu, H., Yu, Q., Ren, J., Hao, C., Ding, H., ... & Zhang, W. (2025). ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation. arXiv preprint arXiv:2505.22159.

15. Xu, X., Hou, Y., Liu, Z., & Song, S. (2025). Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections. arXiv preprint arXiv:2506.16685, NeurIPS 2025.

16. Zhu, X., Huang, B., & Li, Y. (2025). Touch in the Wild: Learning Fine-Grained Manipulation with a Portable Visuo-Tactile Gripper. arXiv preprint arXiv:2507.15062., NeurIPS 2025.

17. Huang, B., Xu, J., Akinola, I., Yang, W., Sundaralingam, B., O'Flaherty, R., ... & Li, Y. (2025). VT-Refine: Learning Bimanual Assembly with Visuo-Tactile Feedback via Simulation Fine-Tuning, CoRL 2025.

18. Sun, Z., & Song, S. (2025). Latent Policy Barrier: Learning Robust Visuomotor Policies by Staying In-Distribution. arXiv preprint arXiv:2508.05941.

19. Zhang, Z., Xu, H., Yang, Z., Yue, C., Lin, Z., Gao, H. A., ... & Zhao, H. (2025). TA-VLA: Elucidating the Design Space of Torque-aware Vision-Language-Action Models. arXiv preprint arXiv:2509.07962, CoRL 2025.

20. Xu, B., Weng, H., Lu, Q., Gao, Y., & Xu, H. (2025). FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots. arXiv preprint arXiv:2505.06883, CoRL 2025.

21. Zhi, P., Li, P., Yin, J., Jia, B., & Huang, S. (2025). Learning Unified Force and Position Control for Legged Loco-Manipulation. arXiv preprint arXiv:2505.20829, CoRL 2025 Best Paper Award.

22. Ge, H., Jia, Y., Li, Z., Li, Y., Chen, Z., Huang, R., & Zhou, G. (2025). FILIC: Dual-Loop Force-Guided Imitation Learning with Impedance Torque Control for Contact-Rich Manipulation Tasks. arXiv preprint arXiv:2509.17053.

23. Zhao, Z., Haldar, S., Cui, J., Pinto, L., & Bhirangi, R. (2025). Touch begins where vision ends: Generalizable policies for contact-rich manipulation. arXiv preprint arXiv:2506.13762.

24. Xiao, W., Lin, H., Peng, A., Xue, H., He, T., Xie, Y., ... & Zhu, Y. (2025). Self-Improving Vision-Language-Action Models with Data Generation via Residual RL. arXiv preprint arXiv:2511.00091.

25. Huang, D., Navab, N., & Jiang, Z. (2025). Improving Robustness to Out-of-Distribution States in Imitation Learning via Deep Koopman-Boosted Diffusion Policy. IEEE Transactions on Robotics.

26. Gong, Z., Lyu, S., Ding, P., Xiao, W., & Wang, D. (2025). Robust Online Residual Refinement via Koopman-Guided Dynamics Modeling. arXiv preprint arXiv:2509.12562.

27. Huang, D., Navab, N., & Jiang, Z. (2025). Improving Robustness to Out-of-Distribution States in Imitation Learning via Deep Koopman-Boosted Diffusion Policy. IEEE Transactions on Robotics.

28. Xu, Z., Miao, Z., Qiu, Q., Zhang, Z., & She, Y. (2025). DiffOG: Differentiable Policy Trajectory Optimization with Generalizability. arXiv preprint arXiv:2504.13807. IEEE Transactions on Robotics.

29. Heng, L., Geng, H., Zhang, K., Abbeel, P., & Malik, J. (2025). ViTacFormer: Learning Cross-Modal Representation for Visuo-Tactile Dexterous Manipulation. arXiv preprint arXiv:2506.15953.

30. Huang, J., Ye, Y., Gong, Y., Zhu, X., Gao, Y., & Zhang, K. (2025). Spatially anchored Tactile Awareness for Robust Dexterous Manipulation. arXiv preprint arXiv:2510.14647.

31. Hsieh, E., Hsieh, W. H., Wang, Y. J., Lin, T., Malik, J., Sreenath, K., & Qi, H. (2025). Learning Dexterous Manipulation Skills from Imperfect Simulations. arXiv preprint arXiv:2512.02011.

32. Suresh, S., Qi, H., Wu, T., Fan, T., Pineda, L., Lambeta, M., ... & Mukadam, M. (2024). NeuralFeels with neural fields: Visuotactile perception for in-hand manipulation. Science Robotics, 9(96), eadl0628.

Agile Robot Control 

33. Kaufmann, E., Bauersfeld, L., Loquercio, A., Müller, M., Koltun, V., & Scaramuzza, D. (2023). Champion-level drone racing using deep reinforcement learning. Nature, 620(7976), 982-987.

34. Song, Y., Romero, A., Müller, M., Koltun, V., & Scaramuzza, D. (2023). Reaching the limit in autonomous racing: Optimal control versus reinforcement learning. Science Robotics, 8(82), eadg1462.

35. Romero, A., Aljalbout, E., Song, Y., & Scaramuzza, D. (2024). Actor-critic model predictive control: Differentiable optimization meets reinforcement learning. arXiv preprint arXiv:2306.09852. (TRO 2026)

36. Luo, S., Jiang, M., Zhang, S., Zhu, J., Yu, S., Dominguez Silva, I., ... & Su, H. (2024). Experiment-free exoskeleton assistance via learning in simulation. Nature, 630(8016), 353-359.

37. Romero, A., Sun, S., Foehn, P., & Scaramuzza, D. (2022). Model predictive contouring control for time-optimal quadrotor flight. IEEE Transactions on Robotics, 38(6), 3340-3356.

38. Wei, M., Zheng, L., Wu, Y., Mei, R., & Cheng, H. (2025). Meta-Learning Enhanced Model Predictive Contouring Control for Agile and Precise Quadrotor Flight. IEEE Transactions on Robotics.

39. Richards, S. M., Azizan, N., Slotine, J. J., & Pavone, M. (2023). Control-oriented meta-learning. The International Journal of Robotics Research, 42(10), 777-797.

40. Saied, H., Chemori, A., Bouri, M., El Rafei, M., & Francis, C. (2023). Feedforward super-twisting sliding mode control for robotic manipulators: Application to PKMs. IEEE Transactions on Robotics, 39(4), 3167-3184.

41. Jia, J., Zhang, W., Guo, K., Wang, J., Yu, X., Shi, Y., & Guo, L. (2023). Evolver: Online learning and prediction of disturbances for robot control. IEEE Transactions on Robotics, 40, 382-402

42. O’Connell, M., Shi, G., Shi, X., Azizzadenesheli, K., Anandkumar, A., Yue, Y., & Chung, S. J. (2022). Neural-fly enables rapid learning for agile flight in strong winds. Science Robotics, 7(66), eabm6597.

43. Jia, J., Yang, Z., Wang, M., Guo, K., Yang, J., Yu, X., & Guo, L. Feedback Favors the Generalization of Neural ODEs. In The Thirteenth International Conference on Learning Representations (ICLR 2025).

44. Wei, L., Feng, H., Yang, Y., Feng, R., Hu, P., Zheng, X., ... & Wu, T. (2024). Closed-loop diffusion control of complex physical systems. In The Thirteenth International Conference on Learning Representations (ICLR 2025).

45. Ye, N., Zeng, Z., Zhou, J., Zhu, L., Duan, Y., Wu, Y., ... & Zhou, C. (2024). OoD-Control: Generalizing Control in Unseen Environments. IEEE Transactions on Pattern Analysis and Machine Intelligence.

46. Xu, B., Weng, H., Lu, Q., Gao, Y., & Xu, H. (2025). FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots. arXiv preprint arXiv:2505.06883, CoRL 2025

47. Zhi, P., Li, P., Yin, J., Jia, B., & Huang, S. (2025). Learning Unified Force and Position Control for Legged Loco-Manipulation. arXiv preprint arXiv:2505.20829, CoRL 2025 Best Paper Award.

48. Yang, L., Werner, B., & Ames, M. D. S. A. D. (2025). CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functions. arXiv preprint arXiv:2510.14959.

49. Nan, F., Ma, H., Guan, Q., Hughes, J., Muehlebach, M., & Hutter, M. (2025). Efficient Model-Based Reinforcement Learning for Robot Control via Online Learning. arXiv preprint arXiv:2510.18518.

50. Zhang, D., Loquercio, A., Tang, J., Wang, T. H., Malik, J., & Mueller, M. W. (2025). A learning-based quadcopter controller with extreme adaptation. IEEE Transactions on Robotics.

Safe Control 

51. He, T., Zhang, C., Xiao, W., He, G., Liu, C., & Shi, G. (2024). Agile but safe: Learning collision-free high-speed legged locomotion. arXiv preprint arXiv:2401.17583.

52. Brunke, L., Greeff, M., Hall, A. W., Yuan, Z., Zhou, S., Panerati, J., & Schoellig, A. P. (2022). Safe learning in robotics: From learning-based control to safe reinforcement learning. Annual Review of Control, Robotics, and Autonomous Systems, 5(1), 411-444.

53. Dawson, C., Gao, S., & Fan, C. (2023). Safe control with learned certificates: A survey of neural lyapunov, barrier, and contraction methods for robotics and control. IEEE Transactions on Robotics, 39(3), 1749-1767.

54. Berkenkamp, F., Schoellig, A. P., & Krause, A. (2016). Safe controller optimization for quadrotors with Gaussian processes. In 2016 IEEE international conference on robotics and automation (ICRA) (pp. 491-496).

55. Ames, A. D., Xu, X., Grizzle, J. W., & Tabuada, P. (2016). Control barrier function based quadratic programs for safety critical systems. IEEE Transactions on Automatic Control, 62(8), 3861-3876.

56. Xiao, W., Wang, T. H., Hasani, R., Chahine, M., Amini, A., Li, X., & Rus, D. (2023). Barriernet: Differentiable control barrier functions for learning of safe robot control. IEEE Transactions on Robotics, 39(3), 2289-2307.

57. So, O., Serlin, Z., Mann, M., Gonzales, J., Rutledge, K., Roy, N., & Fan, C. (2024, May). How to train your neural control barrier function: Learning safety filters for complex input-constrained systems. In 2024 IEEE International Conference on Robotics and Automation (ICRA) (pp. 11532-11539). IEEE.

58. Wang, G., Ren, K., Morgan, A. S., & Hang, K. (2025). Caging in time: A framework for robust object manipulation under uncertainties and limited robot perception. The International Journal of Robotics Research, 02783649251343926.

Robot Compliance Control

59. Haddadin, S., & Shahriari, E. (2024). Unified force-impedance control. The International Journal of Robotics Research, 43(13), 2112-2141.

60. Iskandar, M., Ott, C., Albu-Schäffer, A., Siciliano, B., & Dietrich, A. (2023). Hybrid force-impedance control for fast end-effector motions. IEEE Robotics and Automation Letters, 8(7), 3931-3938.

61. Li, Y., Zheng, L., Wang, Y., Dong, E., & Zhang, S. (2025). Impedance Learning-based Adaptive Force Tracking for Robot on Unknown Terrains. IEEE Transactions on Robotics.

62. Hou, Y., Liu, Z., Chi, C., Cousineau, E., Kuppuswamy, N., Feng, S., ... & Song, S. (2024). Adaptive Compliance Policy: Learning Approximate Compliance for Diffusion Guided Control. arXiv preprint arXiv:2410.09309.

63. Xu, X., Hou, Y., Liu, Z., & Song, S. (2025). Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections. arXiv preprint arXiv:2506.16685.

64. Zhi, P., Li, P., Yin, J., Jia, B., & Huang, S. (2025). Learning Unified Force and Position Control for Legged Loco-Manipulation. arXiv preprint arXiv:2505.20829, CoRL 2025 Best Paper Award.

65. Geiger, N., Asfour, T., Hogan, N., & Lachner, J. (2025). Diffusion-Based Impedance Learning for Contact-Rich Manipulation Tasks. arXiv preprint arXiv:2509.19696.

66. Chen, W., Xue, H., Wang, Y., Zhou, F., Lv, J., Jin, Y., ... & Lu, C. (2025). ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning. arXiv preprint arXiv:2512.10946.

Aerial Manipulation

67. Wang, M., Chen, Z., Guo, K., Yu, X., Zhang, Y., Guo, L., & Wang, W. (2023). Millimeter-level pick and peg-in-hole task achieved by aerial manipulator. IEEE Transactions on Robotics, 40, 1242-1260.

68. He, G., Guo, X., Tang, L., Zhang, Y., Mousaei, M., Xu, J., ... & Shi, G. (2025). Flying Hand: End-Effector-Centric Framework for Versatile Aerial Manipulation Teleoperation and Policy Learning. arXiv preprint arXiv:2504.10334.

69. Li, G., Liu, X., & Loianno, G. (2024). Human-aware physical human-robot collaborative transportation and manipulation with multiple aerial robots. IEEE Transactions on Robotics.

70. Zeng, J., Gimenez, A. M., Vinitsky, E., Alonso-Mora, J., & Sun, S. Decentralized Aerial Manipulation of a Cable-Suspended Load Using Multi-Agent Reinforcement Learning. In 9th Annual Conference on Robot Learning (CoRL 2025).

71. Gupta, H., Guo, X., Ha, H., Pan, C., Cao, M., Lee, D., ... & Shi, G. (2025). UMI-on-Air: Embodiment-Aware Guidance for Embodiment-Agnostic Visuomotor Policies. arXiv preprint arXiv:2510.02614.

72. Sun, S., Wang, X., Sanalitro, D., Franchi, A., Tognon, M., & Alonso-Mora, J. (2025). Agile and Cooperative Aerial Manipulation of a Cable-Suspended Load. arXiv preprint arXiv:2501.18802. Science Robotics.

73. Duan, Y., Li, H., Wu, Y., Yu, W., Zhang, X., Shen, Y., ... & Zhang, Y. (2025). STDArm: Transferring Visuomotor Policies From Static Data Training to Dynamic Robot Manipulation. arXiv preprint arXiv:2504.18792, RSS 2025