90 citations · 175 across the 26 of their papers we have counts for
7 papers · 1 filter
Masked Autoencoders for Egocentric Video Understanding @ Ego4D Challenge 2022
Jiachen Lei, Shuang Ma, Zhongjie Ba +3
In this report, we present our approach and empirical results of applying masked autoencoders in two egocentric video understanding tasks, namely, Object State Change Classificatio…
Learning Modular Simulations for Homogeneous Systems
Jayesh K. Gupta, Sai Vemprala, Ashish Kapoor
Complex systems are often decomposed into modular subsystems for engineering tractability. Although various equation based white-box modeling techniques make use of such structure,…
PACT: Perception-Action Causal Transformer for Autoregressive Robotics Pre-Training
Rogerio Bonatti, Sai Vemprala, Shuang Ma +3
Robotics has long been a field riddled with complex systems architectures whose modules and connections, whether traditional or learning-based, require significant human expertise…
Learning to Simulate Realistic LiDARs
Benoit Guillard, Sai Vemprala, Jayesh K. Gupta +4
Simulating realistic sensors is a challenging part in data generation for autonomous systems, often involving carefully handcrafted sensor design, scene properties, and physics mod…
LATTE: LAnguage Trajectory TransformEr
Arthur Bucker, Luis Figueredo, Sami Haddadin +4
Natural language is one of the most intuitive ways to express human intent. However, translating instructions and commands towards robotic motion generation and deployment in the r…
Missingness Bias in Model Debugging
Saachi Jain, Hadi Salman, Eric Wong +4
Missingness, or the absence of features from an input, is a concept fundamental to many model debugging tools. However, in computer vision, pixels cannot simply be removed from an…
COMPASS: Contrastive Multimodal Pretraining for Autonomous Systems
Shuang Ma, Sai Vemprala, Wenshan Wang +4
Learning representations that generalize across tasks and domains is challenging yet necessary for autonomous systems. Although task-driven approaches are appealing, designing mode…