60 citations · 155 across the 19 of their papers we have counts for
5 papers · 1 filter
CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous Multi-Agent Reinforcement Learning
Vasu Sharma, Prasoon Goyal, Kaixiang Lin +3
We propose a multimodal (vision-and-language) benchmark for cooperative and heterogeneous multi-agent learning. We introduce a benchmark multimodal dataset with tasks involving col…
DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following
Xiaofeng Gao, Qiaozi Gao, Ran Gong +3
Language-guided Embodied AI benchmarks requiring an agent to navigate an environment and manipulate objects typically allow one-way communication: the human user gives a natural la…
LUMINOUS: Indoor Scene Generation for Embodied AI Challenges
Yizhou Zhao, Kaixiang Lin, Zhiwei Jia +4
Learning-based methods for training embodied agents typically require a large number of high-quality scenes that contain realistic layouts and support meaningful interactions. Howe…
Are We There Yet? Learning to Localize in Embodied Instruction Following
Shane Storks, Qiaozi Gao, Govind Thattai +1
Embodied instruction following is a challenging problem requiring an agent to infer a sequence of primitive actions to achieve a goal environment state from complex language and vi…
Interactive Learning of State Representation through Natural Language Instruction and Explanation
Qiaozi Gao, Lanbo She, Joyce Y. Chai
One significant simplification in most previous work on robot learning is the closed-world assumption where the robot is assumed to know ahead of time a complete set of predicates…