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

ICRA 2026 Workshop | Dexterity with Multifingered Hands:Hardware, Sensing, and Skills

CoRL 2025 Livestream

RSS 2025 Livestream

Lumina具身智能社区

Sharpa GTC 2026 Presentation - Unlocking High-DoF Dexterity via Tactile AI

灵巧手、数据、触觉：为什么大家都在聊 Sharpa？

Robot Learning

1. 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

2. 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. (Science Robotics)

3. Yu, D., Lei, K., Jiang, Z., Pan, J., & Xu, H. (2026). Beyond Action Residuals: Real-World Robot Policy Steering via Bottleneck Latent Reinforcement Learning. arXiv preprint arXiv:2605.19919. (Latent steering RL)

4. Yang, J., Lin, K., Li, J., Zhang, W., Lin, T., Wu, L., ... & Li, H. (2026). Rise: Self-improving robot policy with compositional world model. arXiv preprint arXiv:2602.11075. (RSS 2026)

5. Wang, Y., Li, X., Xie, P., Yang, P., Nie, B., Cai, Y., ... & Luo, J. (2026). Learning while Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies. arXiv preprint arXiv:2605.00416.

6. 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.

7. 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.

8. Zhang, H., Huang, W. H., Tong, Q., Solak, G., Liu, P., Liu, S., ... & Ajoudani, A. (2026). CompliantVLA-adaptor: VLM-Guided Variable Impedance Action for Safe Contact-Rich Manipulation. arXiv preprint arXiv:2601.15541.

9. 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, RSS 2025.

10. 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.

11. Fang, H., Tang, S., Mei, M., Qin, H., He, Z., Chen, J., ... & Wang, S. (2026). Force Policy: Learning Hybrid Force-Position Control Policy under Interaction Frame for Contact-Rich Manipulation, RSS 2026.

12. 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, NeurIPS 2025.

13. Li, Y., Zhaxizhuoma, Z., Jiang, H., Xia, J., Zhang, H., Du, J., ... & Pang, J. (2026). ForceVLA2: Unleashing hybrid force-position control with force awareness for contact-rich manipulation, CVPR 2026.

14. 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, CoRL 2025.

15. Yang, Y., Chen, A., Zhu, Z., Xu, K., Mao, Y., Wei, Y., ... & Wang, Y. (2026). Direction Matters: Learning Force Direction Enables Sim-to-Real Contact-Rich Manipulation. arXiv preprint arXiv:2602.14174.

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. Ruan, M., Zhou, L., Li, H., Wang, Z., Lu, Z., Zhang, J., & Fang, B. (2026). ReTac-ACT: A State-Gated Vision-Tactile Fusion Transformer for Precision Assembly. arXiv preprint arXiv:2603.09565. (Manipulation Net Evaluation)

19. Zhang, Z., Ma, J., Yang, X., Wen, X., Zhang, Y., Li, B., ... & Ma, D. (2026). TouchGuide: Inference-Time Steering of Visuomotor Policies via Touch Guidance. arXiv preprint arXiv:2601.20239. (RSS 2026)

20. Zheng, Y., Gu, S., Li, W., Zheng, Y., Zang, Y., Tian, S., ... & Ding, W. (2026). OmniVTA: Visuo-Tactile World Modeling for Contact-Rich Robotic Manipulation. arXiv preprint arXiv:2603.19201.

21. Yuan, H., Yi, W., Zhang, Z., Chen, W., Mo, Y., Yin, J., ... & Lourentzou, I. (2026). Vtam: Video-tactile-action models for complex physical interaction beyond vlas. arXiv preprint arXiv:2603.23481. 

22. Zang, Y., Zheng, Y., Nie, X., Zheng, Y., Tian, S., Gu, S., ... & Ding, W. (2026). TacForeSight: Force-Guided Tactile World Model for Contact-Rich Manipulation. arXiv preprint arXiv:2606.11184. (Tactile World Model)

23. Yu, K., Zhang, H., Ravichandar, H., Han, Y., & Gao, R. (2026). OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies. arXiv preprint arXiv:2607.03723. (Tactile residual RL)

24. Bronars, A., Park, Y., & Agrawal, P. (2026). Tune to Learn: How Controller Gains Shape Robot Policy Learning. arXiv preprint arXiv:2604.02523, RSS 2026 (connecting controller tuning, compliance control and robot learning)

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.

Dexterous Hand Manipulation 

27. 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. (RSS 2026)

28. 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.

29. Liu, X., Wang, H., & Yi, L. (2025). DexNDM: Closing the Reality Gap for Dexterous In-Hand Rotation via Joint-Wise Neural Dynamics Model. arXiv preprint arXiv:2510.08556.

30. Su, L., Peng, Z., Ren, R., Mao, S., Du, J., Zhang, K., & Zhu, X. (2026). Tacmap: Bridging the tactile sim-to-real gap via geometry-consistent penetration depth map. arXiv preprint arXiv:2602.21625.

31. 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

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.

33. Ye, Q., Liu, Q., Wang, S., Chen, J., Cui, Y., Jin, K., ... & Chen, J. (2026). Visual-tactile pretraining and online multitask learning for humanlike manipulation dexterity. Science Robotics.

34. Tang, T., Ji, X., Xing, W., Hao, C., Xu, W., Shao, L., ... & Zhang, K. (2026). Towards Human-Like Manipulation through RL-Augmented Teleoperation and Mixture-of-Dexterous-Experts VLA. arXiv preprint arXiv:2603.08122.

35. Han, Y., Chen, Z., Zhao, Y., Xu, C., Shao, Y., Peng, Y., ... & Lian, W. (2026). DexHiL: A Human-in-the-Loop Framework for Vision-Language-Action Model Post-Training in Dexterous Manipulation. arXiv preprint arXiv:2603.09121.

36. Chen, Z., Han, Y., Shao, Y., Liu, H., Xu, C., Chen, X., ... & Lian, W. (2026). BORA: Bridging Offline Reinforcement Learning and Online Residual Adaptation for Real-World Dexterous VLA Models. arXiv preprint arXiv:2605.30226.

37. Zhang, C., Cai, P., Yuan, H., Xu, C., & Lu, Z. (2025). UniTacHand: Unified Spatio-Tactile Representation for Human to Robotic Hand Skill Transfer. arXiv preprint arXiv:2512.21233. (Glove-to-Robot Tactile Transfer)

38. Chen, C., Yu, Z., Choi, H., Cutkosky, M., & Bohg, J. (2025). Dexforce: Extracting force-informed actions from kinesthetic demonstrations for dexterous manipulation. IEEE Robotics and Automation Letters. (Tactile compliance policy)

39. Xu, Z., Wang, Y., Abbatematteo, B., Preechayasomboon, J., Chan, S., Colonnese, N., & Memar, A. H. (2026). Contact-Grounded Policy: Dexterous Visuotactile Policy with Generative Contact Grounding. arXiv preprint arXiv:2603.05687. (Tactile compliance policy, RSS 2026)

40. Li, Z., Huang, L., Xu, W., Zhu, Z., Lin, N., Ma, X., ... & Wen, R. (2026). Hand-in-the-Loop: Improving Dexterous VLA via Seamless Interventional Correction. arXiv preprint arXiv:2605.15157. (Relative Hand Retargeting)

41. Atar, S., Huang, Y. T., & Yip, M. (2026). Transferring Contact, Not Just Motion: Compliant Grasping Across Dexterous Hands. arXiv preprint arXiv:2606.15516. (Contact-aware Retargeting)

42. Wu, J., Yao, S., He, G., Liu, X., Zeng, Z., Jiang, X., ... & Zhao, H. (2026). TopoRetarget: Interaction-Preserving Retargeting for Dexterous Manipulation. arXiv preprint arXiv:2606.16272. (Hand-object Retargeting)

43. Feng, Y., Leung, N., Wang, J., Yang, L., Qi, H., & Culbertson, P. (2026). A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation. arXiv preprint arXiv:2607.11874. (Retargeting + RL)

44. Guo, C., Chen, X., Zeng, Z., Guo, Z., Li, Y., Xiao, H., ... & Lu, H. (2025). Grasp like humans: Learning generalizable multi-fingered grasping from human proprioceptive sensorimotor integration. IEEE Transactions on Robotics.

45. Niu, D., Liu, Z., Wang, Z., Shao, B., Yin, Z. H., Pai, A., ... & Darrell, T. (2026). T-Rex: Tactile-Reactive Dexterous Manipulation. arXiv preprint arXiv:2606.17055.

46. Yuan, C., Zhang, Z., Zhou, M., Chen, W., Wang, Y., Liu, Z., ... & Gao, Y. (2026). FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation. arXiv preprint arXiv:2606.13102.

47. Zhu, X., Liu, Z., Jain, S., Li, C., Noori, M., Zhao, H., ... & Chang, Y. (2026). Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration. arXiv preprint arXiv:2607.00033. (object-centric | wrench guidance)

48. Ma, C., Yao, Y., Wei, Z., Li, R., Szafir, D., & Ding, M. (2026). Current as Touch: Proprioceptive Contact Feedback for Compliant Dexterous Manipulation. arXiv preprint arXiv:2607.03529. 

Robot Compliance Control

49. 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.

50. Shi, H., Hu, S., Hou, Y., Wang, W., Liu, K., & Shuran Song. (2026). Minimalist Compliance Control. arXiv preprint arXiv:2603.00913.

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

52. 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.

53. 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.

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

Aerial Manipulation

55. 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.

56. 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.

57. 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.

58. 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