Grounding Robot Plans from Natural Language Instructions with Incomplete World Knowledge

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks (2019, background): “Instruction following is a long-standing topic of interest in robotics [7, 10, 29, 34, 39, 40, 46, 51].”
Embodied BERT: A Transformer Model for Embodied, Language-guided Visual Task Completion (2021, background): “Advances in simulators and large-scale data collection have facilitated moving past static language grounding tasks like visual question answering [4, 23] and captioning [80, 65, 36] to embodied benchmarks like Vision-and-Language Navigation (VLN) [49, 17, 3, 61, 41], embodied question answering [22”
Robots That Use Language (2020, methodology): “(71) used a similar probabilistic model to utilize relational knowledge to fill in gaps for aspects of the language missing from the workspace.”
Correcting Robot Plans with Natural Language Feedback (2022, background): “2) Language for Robot Task Speciﬁcation: Natural language has been extensively studied as a means for task speci-ﬁcation or instruction following in robotics [37, 22, 15, 4, 28, 43, 8, 31].”
Learning to Map Natural Language Instructions to Physical Quadcopter Control using Simulated Flight (2019, background): “Nyga et al. [38] studied modeling incomplete information in instructions with a modular approach.”