2 papers
cs.RO2026
Latent Policy Steering through One-Step Flow Policies
Hokyun Im, Andrey Kolobov, Jianlong Fu +1
Offline reinforcement learning (RL) allows robots to learn from offline datasets without risky exploration. Yet, offline RL's performance often hinges on a brittle trade-off betwee…
cs.RO2026
Emergent Dexterity via Diverse Resets and Large-Scale Reinforcement Learning
Patrick Yin, Tyler Westenbroek, Zhengyu Zhang +9
Reinforcement learning in massively parallel physics simulations has driven major progress in sim-to-real robot learning. However, current approaches remain brittle and task-specif…