From Woofs to Words: Towards Intelligent Robotic Guide Dogs with Verbal Communication
- URL: http://arxiv.org/abs/2603.12574v1
- Date: Fri, 13 Mar 2026 02:11:00 GMT
- Title: From Woofs to Words: Towards Intelligent Robotic Guide Dogs with Verbal Communication
- Abstract summary: A robotic guide dog is an assistive quadruped robot that helps visually impaired people in obstacle avoidance and navigation.<n>We develop a novel dialog system for robotic guide dogs that uses LLMs to verbalize both navigational plans and scenes.
- Score: 6.5337939793709445
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Assistive robotics is an important subarea of robotics that focuses on the well-being of people with disabilities. A robotic guide dog is an assistive quadruped robot that helps visually impaired people in obstacle avoidance and navigation. Enabling language capabilities for robotic guide dogs goes beyond naively adding an existing dialog system onto a mobile robot. The novel challenges include grounding language in the dynamically changing environment and improving spatial awareness for the human handler. To address those challenges, we develop a novel dialog system for robotic guide dogs that uses LLMs to verbalize both navigational plans and scenes. The goal is to enable verbal communication for collaborative decision-making within the handler-robot team. In experiments, we conducted a human study to evaluate different verbalization strategies and a simulation study to assess the efficiency and accuracy in navigation tasks.
Related papers
- Ludi${}_{\scriptscriptstyle 0.1}$: An Agentic System for Socially Intelligent Robots [22.26258894927344]
Ludi$_scriptscriptstyle 0.1$ is an agentic system for socially intelligent robots.<n>It integrates interactive speech, multimodal reasoning, memory, navigation, and learned manipulation.<n>Ludi$_scriptstyle 0.1$ demonstrates a practical path toward fluid human-robot collaboration.
arXiv Detail & Related papers (2026-08-22T16:38:59Z) - Human-Robot Mutual Learning through Affective-Linguistic Interaction and Differential Outcomes Training [Pre-Print] [0.3811184252495269]
We test how affective-linguistic communication, in combination with differential outcomes training, affects mutual learning in a human-robot context.
Taking inspiration from child- caregiver dynamics, our human-robot interaction setup consists of a (simulated) robot attempting to learn how best to communicate internal, homeostatically-controlled needs.
arXiv Detail & Related papers (2024-07-01T13:35:08Z) - A Human-Robot Mutual Learning System with Affect-Grounded Language
Acquisition and Differential Outcomes Training [0.1812164955222814]
The paper presents a novel human-robot interaction setup for identifying robot homeostatic needs.
We adopted a differential outcomes training protocol whereby the robot provides feedback specific to its internal needs.
We found evidence that DOT can enhance the human's learning efficiency, which in turn enables more efficient robot language acquisition.
arXiv Detail & Related papers (2023-10-20T09:41:31Z) - 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) - Surfer: Progressive Reasoning with World Models for Robotic Manipulation [51.26109827779267]
We introduce a novel and simple robot manipulation framework, called Surfer.
Surfer treats robot manipulation as a state transfer of the visual scene, and decouples it into two parts: action and scene.
It is based on the world model, treats robot manipulation as a state transfer of the visual scene, and decouples it into two parts: action and scene.
arXiv Detail & Related papers (2023-06-20T07:06:04Z) - HERD: Continuous Human-to-Robot Evolution for Learning from Human
Demonstration [57.045140028275036]
We show that manipulation skills can be transferred from a human to a robot through the use of micro-evolutionary reinforcement learning.
We propose an algorithm for multi-dimensional evolution path searching that allows joint optimization of both the robot evolution path and the policy.
arXiv Detail & Related papers (2022-12-08T15:56:13Z) - Gesture2Path: Imitation Learning for Gesture-aware Navigation [54.570943577423094]
We present Gesture2Path, a novel social navigation approach that combines image-based imitation learning with model-predictive control.
We deploy our method on real robots and showcase the effectiveness of our approach for the four gestures-navigation scenarios.
arXiv Detail & Related papers (2022-09-19T23:05:36Z) - 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) - Understanding Natural Language in Context [13.112390442564442]
We focus on cognitive robots, which have some knowledge-based models of the world and operate by reasoning and planning with this model.
Our goal in this research is to translate natural language utterances into this robot's formalism.
We do so by combining off-the-shelf SOTA language models, planning tools, and the robot's knowledge-base for better communication.
arXiv Detail & Related papers (2022-05-25T11:52:16Z) - Intention Aware Robot Crowd Navigation with Attention-Based Interaction
Graph [3.8461692052415137]
We study the problem of safe and intention-aware robot navigation in dense and interactive crowds.
We propose a novel recurrent graph neural network with attention mechanisms to capture heterogeneous interactions among agents.
We demonstrate that our method enables the robot to achieve good navigation performance and non-invasiveness in challenging crowd navigation scenarios.
arXiv Detail & Related papers (2022-03-03T16:26:36Z) - Self-supervised reinforcement learning for speaker localisation with the
iCub humanoid robot [58.2026611111328]
Looking at a person's face is one of the mechanisms that humans rely on when it comes to filtering speech in noisy environments.
Having a robot that can look toward a speaker could benefit ASR performance in challenging environments.
We propose a self-supervised reinforcement learning-based framework inspired by the early development of humans.
arXiv Detail & Related papers (2020-11-12T18:02:15Z) - Situated Multimodal Control of a Mobile Robot: Navigation through a
Virtual Environment [7.387138888226306]
We present a new interface for controlling a navigation robot in novel environments using coordinated gesture and language.
We use a TurtleBot3 robot with a LIDAR and a camera, an embodied simulation of what the robot has encountered while exploring, and a cross-platform bridge facilitating generic communication.
arXiv Detail & Related papers (2020-07-13T16:37:01Z)
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.