4 papers
Robust Multi-Agent Path Finding under Observation Attacks: A Principled Adversarial-Plus-Smoothing Training Recipe
Riad Ahmed
Decentralized multi-agent path finding (MAPF) routes a team of agents on a shared grid, each acting from its own local view. The standard solution trains one shared neural policy w…
Trajectory-Consistent Flow Matching for Robust Visuomotor Policy Learning
Riad Ahmed, Sujosh Nag, Moniruzzaman Akash +2
Flow matching policies learn continuous velocity fields that transport noise to actions, enabling fast deterministic inference for robot manipulation. However, standard training op…
TAIL-Safe: Task-Agnostic Safety Monitoring for Imitation Learning Policies
Riad Ahmed, Momotaz Begum
Recent imitation learning (IL) algorithms such as flow-matching and diffusion policies demonstrate remarkable performance in learning complex manipulation tasks. However, these pol…
To Do or Not to Do: Ensuring the Safety of Visuomotor Policies Learned from Demonstrations
Riad Ahmed, Moniruzzaman Akash, Momotaz Begum
Task success has historically been the primary measure of policy performance in imitation learning (IL) research. This characteristics strictly limits the ubiquitous applications o…