Skip to main content
Log in

Manipulation skill learning on multi-step complex task based on explicit and implicit curriculum learning

  • Moop
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Conclusion

We proposed a TATD-HER curriculum learning method to learn manipulation skills on multi-step complex tasks. The method addresses the complicated task with both explicit and implicit curriculum learning mechanisms. The experimental results demonstrate the effectiveness of our proposed TATD-HER method and show that the combination of explicit and implicit curriculum learning techniques is crucial for learning complex manipulation skills. Future work involves applying our method to more complex tasks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Liu N-J, Lu T, Cai Y-H, et al. A review of robot manipulation skills learning methods. Acta Autom Sin, 2019, 45: 458–470

    MATH  Google Scholar 

  2. Popov I, Heess N, Lillicrap T, et al. Data-efficient deep reinforcement learning for dexterous manipulation. 2017. ArXiv: 1704.03073

  3. Nair A, McGrew B, Andrychowicz M, et al. Overcoming exploration in reinforcement learning with demonstrations. In: Proceedings of IEEE International Conference on Robotics and Automation, Brisbane, 2018. 6292–6299

  4. Bengio Y, Louradour J, Collobert R, et al. Curriculum learning. In: Proceedings of International Conference on Machine Learning, Montreal, 2009. 41–48

  5. Fournier P, Sigaud O, Chetouani M, et al. Accuracy-based curriculum learning in deep reinforcement learning. 2018. ArXiv: 1806.09614

  6. Andrychowicz M, Wolski F, Ray A, et al. Hindsight experience replay. In: Proceedings of Advances in Neural Information Processing Systems, Long Beach, 2017. 5048–5058

  7. Lillicrap T, Hunt J, Pritzel A, et al. Continuous control with deep reinforcement learning. In: Proceedings of International Conference on Learning Representations, 2016

  8. Schulman J, Wolski F, Dhariwal P, et al. Proximal policy optimization algorithms. 2017. ArXiv: 1707.06347

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61773378, U1713222, U1806204) and Equipment Pre-research Field Fund (Grant No. 61403120407).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tao Lu.

Supporting information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, N., Lu, T., Cai, Y. et al. Manipulation skill learning on multi-step complex task based on explicit and implicit curriculum learning. Sci. China Inf. Sci. 65, 114201 (2022). https://doi.org/10.1007/s11432-019-2648-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-019-2648-7

Navigation