1. 1. Bauza, M., Hogan, F. R., and Rodriguez, A. (2018). "A data-efficient approach to precise and controlled pushing," in Conference on Robot Learning (PMLR), 336-345.
2. A probabilistic data-driven model for planar pushing;Bauza;in 2017 IEEE International Conference on Robotics and Automation (ICRA) (IEEE),2017
3. Object rearrangement through planar pushing: A theoretical analysis and validation;Chai;IEEE Transactions on Robotics,2022
4. 4. Cong, L., Liang, H., Ruppel, P., Shi, Y., G\"orner, M., Hendrich, N., and Zhang, J. (2022). Reinforcement learning with vision-proprioception model for robot planar pushing. Frontiers in Neurorobotics, 16:829437.
5. 5. Coumans, E. and Bai, Y. (2016). Pybullet, a python module for physics simulation for games, robotics and machine learning.