Alejandro Bordallo
Papers
6
Total Citations
68
H-Index
5
About
Alejandro Bordallo’s research lies at the intersection of interactive motion planning, distributed robotics, and human-robot collaboration. His work tackles the fundamental challenge of enabling robots to operate safely and intelligently alongside people in dynamic, unpredictable environments. Bordallo’s most influential contribution, “Counterfactual reasoning about intent for interactive navigation in dynamic environments” (23 citations), introduces a novel framework where robots reason about what other agents *would have done* under different intentions—a breakthrough for scalable, human-aware navigation. He also pioneered the concept of “task variants” in distributed robotics, formalizing how software can adapt to different hardware configurations to balance functional quality and computational cost (papers with 12 and 5 citations). In physical symbol grounding (17 citations), Bordallo combined learning from demonstration with eye tracking to let robots link abstract task symbols to real-world objects, bridging the gap between human instruction and machine execution. His work on efficient collision probability computation (6 citations) and predicting future agent motions (5 citations) further advances safe, anticipatory robot behavior. Together, Bordallo’s contributions—spanning reasoning, grounding, and allocation—are shaping the next generation of autonomous systems that can truly coexist and cooperate with humans.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Solving the task variant allocation problem in distributed robotics12 citations · 2018
- 4
- 5Task Variant Allocation in Distributed Robotics5 citations · 2016
- 6Predicting Future Agent Motions for Dynamic Environments5 citations · 2016