2 citations · 5 across the 6 of their papers we have counts for
6 papers
P3-PO: Prescriptive Point Priors for Visuo-Spatial Generalization of Robot Policies
Mara Levy, Siddhant Haldar, Lerrel Pinto +1
Developing generalizable robot policies that can robustly handle varied environmental conditions and object instances remains a fundamental challenge in robot learning. While consi…
DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control
Zichen Jeff Cui, Hengkai Pan, Aadhithya Iyer +2
Imitation learning has proven to be a powerful tool for training complex visuomotor policies. However, current methods often require hundreds to thousands of expert demonstrations…
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…
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…