2 papers
cs.RO2026
STEAM: Self-Supervised Temporal Ensemble Advantage Modeling for Real-World Robot Learning
Zhihao Liu, Qiuyi Gu, Yitao Wang +16
Real-world robot learning increasingly relies on heterogeneous data, but demonstrations and rollouts often mix useful progress with stalls, corrections, and suboptimal behavior. Ef…
cs.LG2024
Efficient Recurrent Off-Policy RL Requires a Context-Encoder-Specific Learning Rate
Fan-Ming Luo, Zuolin Tu, Zefang Huang +1
Real-world decision-making tasks are usually partially observable Markov decision processes (POMDPs), where the state is not fully observable. Recent progress has demonstrated that…