Collaborative Language Grounding Toward Situated Human-Robot Dialogue

By Joyce Yue Chai, Rui Fang, Changsong Liu, Lanbo She. 2017

52 citations recorded by the source corpus.

To enable situated human-robot dialogue, techniques to support grounded language communication are essential. One particular challenge is to ground human language to robot internal representation of the physical world. Although copresent in a shared environment, humans and robots have mismatched capabilities in reasoning, perception, and action. Their representations of the shared environment and joint tasks are significantly misaligned. Humans and robots will need to make extra effort to bridge the gap and strive for a common ground of the shared world. Only then, is the robot able to engage in language communication and joint tasks. Thus computational models for language grounding will need to take collaboration into consideration. A robot not only needs to incorporate collaborative effort from human partners to better connect human language to its own representation, but also needs to make extra collaborative effort to communicate its representation in language that humans can understand. To address these issues, the Language and Interaction Research group (LAIR) at Michigan State University has investigated multiple aspects of collaborative language grounding. This article gives a brief introduction to this research effort and discusses several collaborative approaches to grounding language to perception and action.

Subfields: Artificial Intelligence, Natural Language Processing, LLM Agents & Tool Use

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Citations in the corpus

Experience Grounds Language (2020, background): “…has on the world, an agent must participate in linguistic activity, such as negotiation (Lewis et al., 2017; He et al., 2018), collaboration (Chai et al., 2017), visual disambiguation (Liu and Chai, 2015; Anderson et al., 2018; Lazaridou et al., 2017), providing emotional support (Rashkin…”
Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learning (2019, background): “On the other hand, experimentation on real humans and robots, despite expensive and time-consuming, are important for understanding the true complexity of real-world scenarios (Chai et al., 2018, 2016; Ryb-ski et al., 2007; Mohan and Laird, 2014; Liu et al., 2016; She et al., 2014).”
Improving Mild Cognitive Impairment Prediction via Reinforcement Learning and Dialogue Simulation (2018, methodology): “In future studies we may look to generate user-specific grounding statements for these slots [4].”
Language to Action: Towards Interactive Task Learning with Physical Agents (2018, background): “Therefore, from the robot perspective, algorithms for language interpretation will need to account for collaborative behaviors from the human, and algorithms for language generation will need to produce collaborative behaviors from the robot [Chai et al. , 2016].”