2 papers
cs.RO2025
Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning
Patrick Yin, Tyler Westenbroek, Simran Bagaria +4
Robot learning requires a considerable amount of high-quality data to realize the promise of generalization. However, large data sets are costly to collect in the real world. Physi…
cs.LG2024
Streetwise Agents: Empowering Offline RL Policies to Outsmart Exogenous Stochastic Disturbances in RTC
Aditya Soni, Mayukh Das, Anjaly Parayil +8
The difficulty of exploring and training online on real production systems limits the scope of real-time online data/feedback-driven decision making. The most feasible approach is…