Page 9 of 13 for All Posts - Page 9

Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning

Adapting to new environments and tasks (e.g., cooking in a new kitchen) requires first gathering information via exploration (e.g., finding the ingredients). We identify a chicken-and-egg coupling problem that prevents existing approaches from effectively exploring, and we solve this with DREAM.

Stanford AI Lab Papers and Talks at ECCV 2020

Compiled by Panos Achlioptas

All the great work from the Stanford AI Lab accepted at ECCV 2020, all in one place.

Stanford AI Lab Papers and Talks at ICML 2020

Compiled by Sidd Karamcheti

All the great work from the Stanford AI Lab accepted at ICML 2020, all in one place.

Stanford Papers and Workshops at RSS 2020

Compiled by Dylan Losey

All of our papers and workshops at Robotics: Science & Systems, all in one place.

Stanford AI Lab Papers and Talks at ACL 2020

Compiled by Ethan A. Chi

All the great work from the Stanford AI Lab accepted at ACL 2020, all in one place.

Back to the Future: Planning-Aware Trajectory Forecasting for Autonomous Driving

Boris Ivanovic

Recent advances in deep generative modeling have brought forth a paradigm shift in trajectory forecasting. In this blog post, we provide an overview of existing work, highlight new opportunities, and present our latest work on developing methods that are cognizant of their downstream use cases.

Stanford AI Lab Papers and Talks at CVPR 2020

Compiled by Andrey Kurenkov

All the great work from the Stanford AI Lab accepted at CVPR 2020, all in one place.

SAIL and Stanford Robotics at ICRA 2020

Compiled by Andrey Kurenkov

All the great work from the Stanford AI Lab accepted at ICRA 2020, all in one place.

Finding Cross-Lingual Syntax in Multilingual BERT

Ethan A. Chi

Large neural language models such as BERT have seen surprising success with multilingual pre-training on a large number of languages. We demonstrate that Multilingual BERT learns cross-lingual syntax, visualizing its inherent structure.

Making Sense of Vision and Touch: Multimodal Representations for Contact-Rich Tasks

Michelle A. Lee

Contact-rich manipulation tasks in unstructured environments often require both tactile and visual feedback. In this blog post, we introduce how to use self-supervision to learn a compact and multimodal representation of vision and touch.