2 papers
cs.AI2026
Let's Think in Two Steps: Mitigating Agreement Bias in MLLMs with Self-Grounded Verification
Moises Andrade, Joonhyuk Cha, Brandon Ho +3
Verifiers--functions assigning rewards to agent behavior--have been key to AI progress in math, code, and games. However, extending gains to domains without clear-cut success crite…
cs.RO2025
Towards Learning Scalable Agile Dynamic Motion Planning for Robosoccer Teams with Policy Optimization
Brandon Ho, Batuhan Altundas, Matthew Gombolay
In fast-paced, ever-changing environments, dynamic Motion Planning for Multi-Agent Systems in the presence of obstacles is a universal and unsolved problem. Be it from path plannin…