Borja Balle

About

Borja Balle is a leading researcher in machine learning and artificial intelligence, whose work bridges foundational theory and practical robotics. His key research areas include reinforcement learning, predictive state representations, and motion prediction for autonomous systems. Balle made significant contributions to modeling pedestrian motion, developing time series models that enable robots to anticipate and safely navigate around dynamic objects like pedestrians and vehicles—a critical capability for real-world deployment. His 2016 paper on this topic has garnered 7 citations, reflecting its influence in robotics and autonomous navigation. Additionally, Balle advanced the theory of Markov decision processes by integrating timing information into learning and planning with temporally extended actions. His 2015 work on using duration predictions to create compact predictive state representations has been cited 3 times, offering a novel approach to handling complex, time-sensitive tasks. Through these contributions, Balle has helped shape how machines learn from and interact with dynamic environments, making his research essential for students and practitioners in AI, robotics, and sequential decision-making.

Research Focus

Key Achievements

Top Papers

1. 1
   Learning time series models for pedestrian motion prediction

   7 citations · 2016
2. 2
   Learning and planning with timing information in Markov decision processes

   3 citations · 2015

Key Collaborators

Contact & Links