Distill: Uncovering the True Intent behind Human-Robot Communication

• URL: http://arxiv.org/abs/2605.14262v1
• Date: Thu, 14 May 2026 02:05:49 GMT
• Title: Distill: Uncovering the True Intent behind Human-Robot Communication
• Abstract summary: We propose the Distill approach for human-robot communication interfaces.<n>Given a task specification, Distill removes unnecessary steps; generalizes the meaning behind individual steps; and relaxes ordering constraints between steps.<n>We implemented Distill on a web interface and, through a crowdsourcing study, demonstrated its ability to elicit and refine user intent from initial task specifications.
• Score: 3.8732212038055827
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As robots become increasingly integrated into everyday environments, intuitive communication paradigms such as natural language and end-user programming have become indispensable for specifying autonomous robot behavior. However, these mechanisms are ineffective at fully capturing user intent: natural language is imprecise and ambiguous, whereas end-user programming can be overly specific. As a result, understanding what users truly mean when they interact with robots remains a central challenge for human-AI communication systems. To address this issue, we propose the Distill approach for human-robot communication interfaces. Given a task specification provided by the user, Distill (1) removes unnecessary steps; (2) generalizes the meaning behind individual steps; and (3) relaxes ordering constraints between steps. We implemented Distill on a web interface and, through a crowdsourcing study, demonstrated its ability to elicit and refine user intent from initial task specifications.

Related papers

• Hierarchical Policies from Verbal and Egocentric Human Signals for Natural Human-Robot Interaction [28.306877525705236]
  We present EDITH, a robot framework that captures the human's nonverbal signals through continuous streams of first-person view and gaze from smart glasses.<n>Our hardware system streams the human's first-person view, gaze, and speech to the robot in real time, transcribing the speech into language instructions.<n>In experiments on human-robot interactive tasks, EDITH enables the robot to act on the human's nonverbal signals even when intent is expressed only briefly.
  arXiv  Detail & Related papers  (2026-06-09T00:50:29Z)
• Intuitive Programming, Adaptive Task Planning, and Dynamic Role Allocation in Human-Robot Collaboration [28.96294944351617]
  This review identifies and connects key components enabling intuitive information exchange and skill transfer between humans and robots.<n>We examine the full interaction pipeline: from the human-to-robot communication bridge translating multimodal inputs into robot-understandable representations.
  arXiv  Detail & Related papers  (2025-11-11T19:49:30Z)
• Interpretable Robot Control via Structured Behavior Trees and Large Language Models [0.14990005092937678]
  This paper presents a novel framework that bridges natural language understanding and robotic execution.<n>The proposed approach is practical in real-world scenarios, with an average cognition-to-execution accuracy of approximately 94%.
  arXiv  Detail & Related papers  (2025-08-13T08:53:13Z)
• Bootstrapping Human-Like Planning via LLMs [0.873811641236639]
  Two common end-user programming paradigms include drag-and-drop interfaces and natural language programming.<n>We construct a large language model (LLM)-based pipeline that accepts natural language as input and produces human-like action sequences as output.<n>Although our results reveal that larger models tend to outperform smaller ones in the production of human-like action sequences, smaller models nonetheless achieve satisfactory performance.
  arXiv  Detail & Related papers  (2025-06-27T20:00:51Z)
• Real-time Addressee Estimation: Deployment of a Deep-Learning Model on the iCub Robot [52.277579221741746]
  Addressee Estimation is a skill essential for social robots to interact smoothly with humans. Inspired by human perceptual skills, a deep-learning model for Addressee Estimation is designed, trained, and deployed on an iCub robot. The study presents the procedure of such implementation and the performance of the model deployed in real-time human-robot interaction.
  arXiv  Detail & Related papers  (2023-11-09T13:01:21Z)
• HandMeThat: Human-Robot Communication in Physical and Social Environments [73.91355172754717]
  HandMeThat is a benchmark for a holistic evaluation of instruction understanding and following in physical and social environments. HandMeThat contains 10,000 episodes of human-robot interactions. We show that both offline and online reinforcement learning algorithms perform poorly on HandMeThat.
  arXiv  Detail & Related papers  (2023-10-05T16:14:46Z)
• Open-World Object Manipulation using Pre-trained Vision-Language Models [72.87306011500084]
  For robots to follow instructions from people, they must be able to connect the rich semantic information in human vocabulary. We develop a simple approach, which leverages a pre-trained vision-language model to extract object-identifying information. In a variety of experiments on a real mobile manipulator, we find that MOO generalizes zero-shot to a wide range of novel object categories and environments.
  arXiv  Detail & Related papers  (2023-03-02T01:55:10Z)
• "No, to the Right" -- Online Language Corrections for Robotic Manipulation via Shared Autonomy [70.45420918526926]
  We present LILAC, a framework for incorporating and adapting to natural language corrections online during execution. Instead of discrete turn-taking between a human and robot, LILAC splits agency between the human and robot. We show that our corrections-aware approach obtains higher task completion rates, and is subjectively preferred by users.
  arXiv  Detail & Related papers  (2023-01-06T15:03:27Z)
• Robots with Different Embodiments Can Express and Influence Carefulness in Object Manipulation [104.5440430194206]
  This work investigates the perception of object manipulations performed with a communicative intent by two robots. We designed the robots' movements to communicate carefulness or not during the transportation of objects.
  arXiv  Detail & Related papers  (2022-08-03T13:26:52Z)
• Whither the Priors for (Vocal) Interactivity? [6.709659274527638]
  Speech-based communication is often cited as one of the most natural' ways in which humans and robots might interact. Despite this, the resulting interactions are anything but natural' It is argued here that such communication failures are indicative of a deeper malaise.
  arXiv  Detail & Related papers  (2022-03-16T12:06:46Z)
• Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation [80.29069988090912]
  We study the problem of learning a range of vision-based manipulation tasks from a large offline dataset of robot interaction. We propose to leverage offline robot datasets with crowd-sourced natural language labels. We find that our approach outperforms both goal-image specifications and language conditioned imitation techniques by more than 25%.
  arXiv  Detail & Related papers  (2021-09-02T17:42:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.