3 papers
cs.LG2026
Steerable Pluralistic Policy Alignment from Anonymized Crowd Feedback
Akansha Kalra, Ellen R. Novoseller, Vernon J. Lawhern +1
Pluralistic alignment requires AI systems to learn from diverse populations whose preferences may reflect heterogeneous or even conflicting objectives, and to personalize their beh…
cs.RO2026
MO-Playground: Massively Parallelized Multi-Objective Reinforcement Learning for Robotics
Neil Janwani, Ellen Novoseller, Vernon J. Lawhern +1
Multi-objective reinforcement learning (MORL) is a powerful tool to learn Pareto-optimal policy families across conflicting objectives. However, unlike traditional RL algorithms, e…
cs.RO2025
R2BC: Multi-Agent Imitation Learning from Single-Agent Demonstrations
Connor Mattson, Varun Raveendra, Ellen Novoseller +3
Imitation Learning (IL) is a natural way for humans to teach robots, particularly when high-quality demonstrations are easy to obtain. While IL has been widely applied to single-ro…