163 citations · 420 across the 27 of their papers we have counts for
5 papers · 1 filter
Cross-Domain Transfer via Semantic Skill Imitation
Karl Pertsch, Ruta Desai, Vikash Kumar +4
We propose an approach for semantic imitation, which uses demonstrations from a source domain, e.g. human videos, to accelerate reinforcement learning (RL) in a different target do…
PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection
Shivin Dass, Karl Pertsch, Hejia Zhang +3
Large-scale data is an essential component of machine learning as demonstrated in recent advances in natural language processing and computer vision research. However, collecting l…
Skill-based Model-based Reinforcement Learning
Lucy Xiaoyang Shi, Joseph J. Lim, Youngwoon Lee
Model-based reinforcement learning (RL) is a sample-efficient way of learning complex behaviors by leveraging a learned single-step dynamics model to plan actions in imagination. H…
Skill-based Meta-Reinforcement Learning
Taewook Nam, Shao-Hua Sun, Karl Pertsch +2
While deep reinforcement learning methods have shown impressive results in robot learning, their sample inefficiency makes the learning of complex, long-horizon behaviors with real…
Task-Induced Representation Learning
Jun Yamada, Karl Pertsch, Anisha Gunjal +1
In this work, we evaluate the effectiveness of representation learning approaches for decision making in visually complex environments. Representation learning is essential for eff…