Collection of papers, blog posts, and articles on reinforcement learning, deep learning and their applications in robotics with special emphasis on legged robotics.

• A. M. Turing, “Computing Machinery and Intelligence,” Mind 59, no. 236 (1950): 433-460.
• Vladimir Vapnik and Corinna Cortes, “Support-Vector Networks,” Machine Learning 20, no. 3 (1995): 273–297.
• Vladimir Vapnik and Alexey Chervonenkis, “A Note on One Class of Perceptrons,” Automation and Remote Control 25 (1964).
• Long short-term memory (1997), S. Hochreiter and J. Schmidhuber.
• Learning Representations by Back-propagating Errors by Rumelhart, Hinton and Williams.
• Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15(56), 1929–1958.
• Yoshua Bengio, Patrice Simard, and Paolo Frasconi, “Learning Long-Term Dependencies with Gradient Descent Is Difficult,” IEEE Transactions on Neural Networks 5, no. 2 (1994).
• Sepp Hochreiter and Jürgen Schmidhuber, “Long Short-Term Memory,” Neural Computation 9, no. 8 (1997).
• Junyoung Chung et al., “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling,” Conference on Neural Information Processing Systems (2014), https://arxiv.org/abs/1412.3555.
• Christian Szegedy et al., “Going Deeper with Convolutions,” Conference on Computer Vision and Pattern Recognition (2014), https://arxiv.org/abs/1409.4842.
• Kaiming He et al., “Deep Residual Learning for Image Recognition,” Conference on Computer Vision and Pattern Recognition (2015), https://arxiv.org/abs/1512.03385.
• Min Lin, Qiang Chen, and Shuicheng Yan, “Network in Network,” International Conference on Learning Representations (2013), https://arxiv.org/abs/1312.4400.
• Sergey Ioffe and Christian Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” Proceedings of the 32nd International Conference on Machine Learning (2015), https://arxiv.org/abs/1502.03167.
• Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge, “A Neural Algorithm of Artistic Style,” arXiv (2015), https://arxiv.org/abs/1508.06576.
• Ian Goodfellow et al., “Generative Adversarial Networks,” arXiv (2014), https://arxiv.org/abs/1406.2661.

• Fu, Z., Kumar, A., Malik, J., & Pathak, D. (2021). Minimizing Energy Consumption Leads to the Emergence of Gaits in Legged Robots. doi:10.48550/ARXIV.2111.01674
  • Reimplementation
  • Review
• Imai, C., Zhang, M., Zhang, Y., Kierebinski, M., Yang, R., Qin, Y., & Wang, X. (n.d.). Vision-Guided Quadrupedal Locomotion in the Wild with Multi-Modal Delay Randomization. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://arxiv.org/abs/2109.14549
• Agarwal, A., Kumar, A., Malik, J., & Pathak, D. (2022). Legged Locomotion in Challenging Terrains using Egocentric Vision. doi:10.48550/ARXIV.2211.07638, https://arxiv.org/abs/2211.07638