most citedIGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022

6 citations · 15 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI20221 cited

Learning to Solve Voxel Building Embodied Tasks from Pixels and Natural Language Instructions

Alexey Skrynnik, Zoya Volovikova, Marc-Alexandre Côté +9

The adoption of pre-trained language models to generate action plans for embodied agents is a promising research strategy. However, execution of instructions in real or simulated e…

cs.CL20226 cited

IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022

Julia Kiseleva, Alexey Skrynnik, Artem Zholus +14

Human intelligence has the remarkable ability to adapt to new tasks and environments quickly. Starting from a very young age, humans acquire new skills and learn how to solve new t…

cs.CL20225 cited

Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021

Julia Kiseleva, Ziming Li, Mohammad Aliannejadi +18

Human intelligence has the remarkable ability to quickly adapt to new tasks and environments. Starting from a very young age, humans acquire new skills and learn how to solve new t…

cs.LG2021

Multitask Adaptation by Retrospective Exploration with Learned World Models

Artem Zholus, Aleksandr I. Panov

Model-based reinforcement learning (MBRL) allows solving complex tasks in a sample-efficient manner. However, no information is reused between the tasks. In this work, we propose a…

cs.AI20213 cited

NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment

Julia Kiseleva, Ziming Li, Mohammad Aliannejadi +12

Human intelligence has the remarkable ability to adapt to new tasks and environments quickly. Starting from a very young age, humans acquire new skills and learn how to solve new t…