Real-time natural language corrections for assistive robotic manipulators

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End-to-End Driving Via Conditional Imitation Learning (2017, background): “Human guidance of robot actions has been studied extensively [5], [14], [24], [34], [35].”
Efficient grounding of abstract spatial concepts for natural language interaction with robot platforms (2018, background): “Additional examples of physical demonstrations of natural language interfaces to robots based on variations of the DCG on robot torsos, unmanned ground vehicles, and assistive robotic manipulators are described in [18], [19], and [20].”
Executing Instructions in Situated Collaborative Interactions (2019, methodology): “…of natural language utterances has been addressed using semantic parsing (e.g., Miller et al., 1996; MacMahon et al., 2006; Chen and Mooney, 2011; Artzi and Zettle-moyer, 2013; Artzi et al., 2014; Long et al., 2016; Iyyer et al., 2017; Suhr et al., 2018; Arkin et al., 2017; Broad et al., 2017).”
Language to Rewards for Robotic Skill Synthesis (2023, methodology): “enable efficient online corrections using distributed correspondence graphs to ground language [44].”