Reinforcement Learning for Language Grounding: Mapping Words to Actions in Human-Robot Interaction
- Title
- Reinforcement Learning for Language Grounding: Mapping Words to Actions in Human-Robot Interaction
- Creator
- Adapa S.R.; Gupta H.; Mukherjee S.; Mallupalli P.R.; Avhad P.; Upreti K.
- Description
- Within the domain of human-robot communication, effective communication is paramount for seamless and smooth collaboration between humans and robots. A promising method for improving language grounding is reinforcement learning (RL), which enables robots to translate spoken commands into suitable behaviors. This paper presents a comprehensive review of recent advancements in RL techniques applied to the task of language grounding in human-robot interaction, focusing specifically on instruction following. Key challenges in this domain include the ambiguity of natural language, the complexity of action spaces, and the need for robust and interpretable models. Various RL algorithms and architectures tailored for language grounding tasks are discussed, highlighting their strengths and limitations. Furthermore, real-world applications and experimental results are examined, showcasing the effectiveness of RL-based approaches in enabling robots to understand and execute instructions from human users. Finally, promising directions for future research are identified, emphasizing the importance of addressing scalability, generalization, and adaptability in RL-based language grounding systems for human-robot interaction. 2024 IEEE.
- Source
- 2024 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024 - Proceedings, pp. 447-452.
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Action-observation mapping; Human-robot interaction; Language grounding; Natural language understanding; Reinforcement learning
- Coverage
- Adapa S.R., OpenLogix LLC, Software Engineer, MI, United States; Gupta H., Software Engineer Meta, NJ, United States; Mukherjee S., VIT Institute, Electrical and Electronics Engg., Tamil Nadu, India; Mallupalli P.R., Arcesium, Hyderabad, India; Avhad P., Veermata Jijabai Technological Inst., Computer Engineering, Mumbai, India; Upreti K., Christ University, Dept. of Computer Science, Delhi NCR Campus, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835035326-6
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Adapa S.R.; Gupta H.; Mukherjee S.; Mallupalli P.R.; Avhad P.; Upreti K., “Reinforcement Learning for Language Grounding: Mapping Words to Actions in Human-Robot Interaction,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/18983.