Overparametrization helps offline-to-online generalization of closed-loop control from pixels
M. Lechner, R. Hasani, A. Amini, T.H. Wang, T.A. Henzinger, D. Rus, in:, Proceedings of the 2024 IEEE International Conference on Robotics and Automation, Institute of Electrical and Electronics Engineers, 2024, pp. 2774–2782.

Learning deposition policies for fused multi-material 3D printing
K. Liao, T. Tricard, M. Piovarci, H.-P. Seidel, V. Babaei, in:, 2023 IEEE International Conference on Robotics and Automation, IEEE, 2023, pp. 12345–12352.

Latent imagination facilitates zero-shot transfer in autonomous racing
A. Brunnbauer, L. Berducci, A. Brandstatter, M. Lechner, R. Hasani, D. Rus, R. Grosu, in:, 2022 International Conference on Robotics and Automation, IEEE, 2022, pp. 7513–7520.

Adversarial training is not ready for robot learning
M. Lechner, R. Hasani, R. Grosu, D. Rus, T.A. Henzinger, in:, 2021 IEEE International Conference on Robotics and Automation, 2021, pp. 4140–4147.

Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme
M. Lechner, R. Hasani, D. Rus, R. Grosu, in:, Proceedings - IEEE International Conference on Robotics and Automation, IEEE, 2020, pp. 5446–5452.