Robot Skill Generalization via Keypoint Integrated Soft Actor-Critic Gaussian Mixture Models
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-63596-0_15
Reference38 articles.
1. Jang, E., et al.: Bc-z: zero-shot task generalization with robotic imitation learning. In: Conference on Robot Learning (2022)
2. von Hartz, J.O., Chisari, E., Welschehold, T., Burgard, W., Boedecker, J. Valada, A.: The treachery of images: bayesian scene keypoints for deep policy learning in robotic manipulation. arXiv preprint arXiv:2305.04718 (2023)
3. Gao, J., Tao, Z., Jaquier, N., Asfour, T.: K-vil: keypoints-based visual imitation learning. arXiv preprint arXiv:2209.03277 (2022)
4. Nematollahi, I., Rosete-Beas, E., Röfer, A., Welschehold, T., Valada, A., Burgard, W.: Robot skill adaptation via soft actor-critic gaussian mixture models. In: 2022 International Conference on Robotics and Automation (ICRA) (2022)
5. Nair, A., Zhu, B., Narayanan, G., Solowjow, E., Levine, S.: Learning on the job: self-rewarding offline-to-online finetuning for industrial insertion of novel connectors from vision. arXiv preprint arXiv:2210.15206 (2022)
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