384 citations · 1.5k across the 50 of their papers we have counts for
56 papers
Hierarchical Latent Action Model
Hanjung Kim, Lerrel Pinto, Seon Joo Kim
Latent Action Models (LAMs) enable learning from actionless data for applications ranging from robotic control to interactive world models. However, existing LAMs typically focus o…
Hierarchical State Space Models for Continuous Sequence-to-Sequence Modeling
Raunaq Bhirangi, Chenyu Wang, Venkatesh Pattabiraman +4
Reasoning from sequences of raw sensory data is a ubiquitous problem across fields ranging from medical devices to robotics. These problems often involve using long sequences of ra…
See to Touch: Learning Tactile Dexterity through Visual Incentives
Irmak Guzey, Yinlong Dai, Ben Evans +2
Equipping multi-fingered robots with tactile sensing is crucial for achieving the precise, contact-rich, and dexterous manipulation that humans excel at. However, relying solely on…
Train Offline, Test Online: A Real Robot Learning Benchmark
Gaoyue Zhou, Victoria Dean, Mohan Kumar Srirama +9
Three challenges limit the progress of robot learning research: robots are expensive (few labs can participate), everyone uses different robots (findings do not generalize across l…
Improving Long-Horizon Imitation Through Instruction Prediction
Joey Hejna, Pieter Abbeel, Lerrel Pinto
Complex, long-horizon planning and its combinatorial nature pose steep challenges for learning-based agents. Difficulties in such settings are exacerbated in low data regimes where…
NetHack is Hard to Hack
Ulyana Piterbarg, Lerrel Pinto, Rob Fergus
Neural policy learning methods have achieved remarkable results in various control problems, ranging from Atari games to simulated locomotion. However, these methods struggle in lo…