Federico Pizarro Bejarano — accepted AI-conference papers

2025

ProDapt: Proprioceptive Adaptation Using Long-Term Memory Diffusion

Federico Pizarro Bejarano, Bryson Jones, Daniel Pastor Moreno, Joseph Bowkett, Paul G. Backes, Angela P. Schoellig

ICRA 2025

Diffusion models have revolutionized imitation learning, allowing robots to replicate complex behaviours. However, diffusion often relies on cameras and other exteroceptive sensors to observe the environment and lacks long-term memory. In space, military, and underwater applications, robots must be

Cited by 0 Source ↗code ↗Scholar ↗

2025

Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement Learning Agents

Federico Pizarro Bejarano, Lukas Brunke, Angela P. Schoellig

RA-L 2025

Reinforcement learning (RL) controllers are flexible and performant but rarely guarantee safety. Safety filters impart hard safety guarantees to RL controllers while maintaining flexibility. However, safety filters can cause undesired behaviours due to the separation between the controller and the s

Cited by 16 Source ↗code ↗Scholar ↗

2023

Deep Reinforcement Learning for Decentralized Multi-Robot Exploration With Macro Actions

Aaron Hao Tan, Federico Pizarro Bejarano, Yuhan Zhu, Richard Ren, Goldie Nejat

RA-L 2023

Cooperative multi-robot teams need to be able to explore cluttered and unstructured environments while dealing with communication dropouts that prevent them from exchanging local information to maintain team coordination. Therefore, robots need to consider high-level teammate intentions during actio

Cited by 53 Source ↗Scholar ↗