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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…
Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play
Irmak Guzey, Ben Evans, Soumith Chintala +1
Teaching dexterity to multi-fingered robots has been a longstanding challenge in robotics. Most prominent work in this area focuses on learning controllers or policies that either…
Teach a Robot to FISH: Versatile Imitation from One Minute of Demonstrations
Siddhant Haldar, Jyothish Pari, Anant Rai +1
While imitation learning provides us with an efficient toolkit to train robots, learning skills that are robust to environment variations remains a significant challenge. Current a…