title: Generative Expressive Robot Behaviors using Large Language Models
summary: People employ expressive behaviors to effectively communicate and coordinate
  their actions with others, such as nodding to acknowledge a person glancing at
  them or saying "excuse me" to pass people in a busy corridor. We would like
  robots to also demonstrate expressive behaviors in human-robot interaction.
  Prior work proposes rule-based methods that struggle to scale to new
  communication modalities or social situations, while data-driven methods
  require specialized datasets for each social situation the robot is used in. We
  propose to leverage the rich social context available from large language
  models (LLMs) and their ability to generate motion based on instructions or
  user preferences, to generate expressive robot motion that is adaptable and
  composable, building upon each other. Our approach utilizes few-shot
  chain-of-thought prompting to translate human language instructions into
  parametrized control code using the robot's available and learned skills.
  Through user studies and simulation experiments, we demonstrate that our
  approach produces behaviors that users found to be competent and easy to
  understand. Supplementary material can be found at
  https://generative-expressive-motion.github.io/.
paper_authors._id: 65b716f8d73977130c7070e2
paper_authors.name: Karthik Mahadevan
paper_authors._id: 65b716f8d73977130c7070e3
paper_authors.name: Jonathan Chien
paper_authors._id: 65b716f8d73977130c7070e4
paper_authors.name: Noah Brown
paper_authors._id: 65b716f8d73977130c7070e5
paper_authors.name: Zhuo Xu
paper_authors._id: 65b716f8d73977130c7070e6
paper_authors.name: Carolina Parada
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-01-29T15:40:41.200Z
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paper_authors.fullname: Carolina Parada
paper_authors.type: user
paper_authors.user: CarolinaParada
paper_authors._id: 65b716f8d73977130c7070e7
paper_authors.name: Fei Xia
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-01-29T08:24:28.892Z
paper_authors._id: 62cfa9ee9b96f22525c36d47
paper_authors.avatarUrl: /avatars/8bfe3bf63f0609b3b6b7d7c5d7f6b3e9.svg
paper_authors.fullname: Fei Xia
paper_authors.type: user
paper_authors.user: fxia22
paper_authors._id: 65b716f8d73977130c7070e8
paper_authors.name: Andy Zeng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-29T11:46:19.606Z
paper_authors._id: 62d5f9d705461e9459d20b91
paper_authors.avatarUrl: /avatars/120917da2d8587674505163fb960e27d.svg
paper_authors.fullname: Andy Zeng
paper_authors.type: user
paper_authors.user: andyzeng
paper_authors._id: 65b716f8d73977130c7070e9
paper_authors.name: Leila Takayama
paper_authors._id: 65b716f8d73977130c7070ea
paper_authors.name: Dorsa Sadigh
paper_title: Generative Expressive Robot Behaviors using Large Language Models
paper_summary: People employ expressive behaviors to effectively communicate and coordinate
  their actions with others, such as nodding to acknowledge a person glancing at
  them or saying "excuse me" to pass people in a busy corridor. We would like
  robots to also demonstrate expressive behaviors in human-robot interaction.
  Prior work proposes rule-based methods that struggle to scale to new
  communication modalities or social situations, while data-driven methods
  require specialized datasets for each social situation the robot is used in. We
  propose to leverage the rich social context available from large language
  models (LLMs) and their ability to generate motion based on instructions or
  user preferences, to generate expressive robot motion that is adaptable and
  composable, building upon each other. Our approach utilizes few-shot
  chain-of-thought prompting to translate human language instructions into
  parametrized control code using the robot's available and learned skills.
  Through user studies and simulation experiments, we demonstrate that our
  approach produces behaviors that users found to be competent and easy to
  understand. Supplementary material can be found at
  https://generative-expressive-motion.github.io/.
paper_ai_summary: The approach uses large language models to generate adaptable expressive robot motion from human instructions through few-shot learning.
paper_ai_keywords: large language models few-shot chain-of-thought prompting parametrized control code expressive robot motion user studies simulation experiments