Reinforcement Learning for robotics
Reinforcement learning (RL) applied to robotics involves training autonomous agents (robots) to learn tasks through interaction with their environment, aiming to maximize cumulative rewards. This approach enables robots to adapt and improve their actions based on feedback received from the environment. RL in robotics is crucial for tasks like robotic manipulation, navigation, and control, where traditional programming methods are challenging due to complex, dynamic environments. Our research group specializes in advancing RL techniques tailored for robotics applications. Explore our publications to discover innovative solutions and insights driving the integration of RL into real-world robotic systems.
Our latest publications in this research area
2024
- Wiebe, F., Turcato, N., Dalla Libera, A., Zhang, C., Vincent, T., Vyas, S., ... & Kumar, S. (2024). Reinforcement learning for athletic intelligence: Lessons from the 1st “ai olympics with realaigym” competition,”. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24, K. Larson, Ed. International Joint Conferences on Artificial Intelligence Organization (Vol. 8, pp. 8833-8837).
- Turcato, N., Libera, A. D., Giacomuzzo, G., Carli, R., & Romeres, D. (2024). Learning control of underactuated double pendulum with Model-Based Reinforcement Learning. arXiv preprint arXiv:2409.05811.
- Calì, M., Sinigaglia, A., Turcato, N., Carli, R., & Susto, G. A. (2024). AI Olympics challenge with Evolutionary Soft Actor Critic. arXiv preprint arXiv:2409.01104.
2023
People Involved
Gian Antonio Susto
Associate Professor
gianantonio.susto@unipd.itRL algorithms, Rl for manufacturing, RL for smart mobility, RL for robotics, RL for telecommunication, RL for HVAC&R
