A Novel Pose Estimation Method of Object in Robotic Manipulation Using Vision-Based Tactile Sensor
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Publisher
Springer Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-16-2336-3_24
Reference20 articles.
1. Fazeli, N., Oller, M., Wu, J., Wu, Z., Tenenbaum, J., Rodriguez, A.: See, feel, act: hierarchical learning for complex manipulation skills with multisensory fusion. Sci. Robot. 4(26), eaav3123 (2019)
2. Lars, K., Nick, H., Tom, D., Marc, H., Tomas, K.: Artificial intelligence for long-term robot autonomy: a survey. IEEE Robot. Autom. Lett. 3, 4023–4030 (2018)
3. Hansen, E., Andersen, R., Madsen, S., Bøgh, S.: Transferring human manipulation knowledge to robots with inverse reinforcement learning. In: Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020, pp. 933–937. Hawaii Convention Center, Honolulu, United States (2020)
4. Park, H., Park, J., Lee, D., Park, J., Baeg, M., Bae, J.: Compliance-based robotic peg-in-hole assembly strategy without force feedback. IEEE Trans. Industr. Electron. 64(8), 6299–6309 (2017)
5. Chan, Y., Yu, H., Khurshid, R.: Effects of force-torque and tactile haptic modalities on classifying the success of robot manipulation tasks. In: 2019 IEEE World Haptics Conference, pp. 586–591. IEEE, Tokyo, Japan (2019)
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