21 citations · 38 across the 10 of their papers we have counts for
7 papers · 1 filter
BAKU: An Efficient Transformer for Multi-Task Policy Learning
Siddhant Haldar, Zhuoran Peng, Lerrel Pinto
Training generalist agents capable of solving diverse tasks is challenging, often requiring large datasets of expert demonstrations. This is particularly problematic in robotics, w…
OPEN TEACH: A Versatile Teleoperation System for Robotic Manipulation
Aadhithya Iyer, Zhuoran Peng, Yinlong Dai +4
Open-sourced, user-friendly tools form the bedrock of scientific advancement across disciplines. The widespread adoption of data-driven learning has led to remarkable progress in m…
PolyTask: Learning Unified Policies through Behavior Distillation
Siddhant Haldar, Lerrel Pinto
Unified models capable of solving a wide variety of tasks have gained traction in vision and NLP due to their ability to share regularities and structures across tasks, which impro…
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…
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…