Related papers: A Human-friendly Verbal Communication Platform for…
To facilitate natural and intuitive interactions with diverse user groups in real-world settings, social robots must be capable of addressing the varying requirements and expectations of these groups while adapting their behavior based on…
Socially Assistive Robots navigate highly sensible environments, which place high demands on safety and communication with users. The reasoning behind an SAR's actions must be transparent at any time to earn users' trust and acceptance.…
A few decades of work in the AI field have focused efforts on developing a new generation of systems which can acquire knowledge via interaction with the world. Yet, until very recently, most such attempts were underpinned by research which…
Long-term deployment of a fleet of mobile robots requires reliable and secure two-way communication channels between individual robots and remote human operators for supervision and tasking. Existing open-source solutions to this problem…
Efficient and intuitive Human-Robot interfaces are crucial for expanding the user base of operators and enabling new applications in critical areas such as precision agriculture, automated construction, rehabilitation, and environmental…
We investigate the use of Large Language Models (LLMs) to equip neural robotic agents with human-like social and cognitive competencies, for the purpose of open-ended human-robot conversation and collaboration. We introduce a modular and…
Effective multi-robot teams require the ability to move to goals in complex environments in order to address real-world applications such as search and rescue. Multi-robot teams should be able to operate in a completely decentralized…
TalkWithMachines aims to enhance human-robot interaction by contributing to interpretable industrial robotic systems, especially for safety-critical applications. The presented paper investigates recent advancements in Large Language Models…
As human-robot collaboration is becoming more widespread, there is a need for a more natural way of communicating with the robot. This includes combining data from several modalities together with the context of the situation and background…
This research investigates strategies for multi-robot coordination in multi-human environments. It proposes a multi-objective learning-based coordination approach to addressing the problem of path planning, navigation, task scheduling, task…
The most common way for robots to handle environmental information is by using maps. At present, each kind of data is hosted on a separate map, which complicates planning because a robot attempting to perform a task needs to access and…
As social robots see increasing deployment within the general public, improving the interaction with those robots is essential. Spoken language offers an intuitive interface for the human-robot interaction (HRI), with dialogue management…
With the rapid explosion of the World Wide Web, it is becoming increasingly possible to easily acquire a wide variety of information such as flight schedules, yellow pages, used car prices, current stock prices, entertainment event…
This paper presents a communication framework built to simplify the construction of robotic ecologies, i.e., networks of heterogeneous computational nodes interfaced with sensors, actuators, and mobile robots. Building integrated ambient…
Multi-robot systems are an efficient method to explore and map an unknown environment. The simulataneous localization and mapping (SLAM) algorithm is common for single robot systems, however multiple robots can share respective map data in…
Recent development in developing humanoid robot poses new challenges to human-machine interaction communication. A major challenge is to develop robots that can behave like and interact with human in the most natural way possible. This…
One of the most challenging coordination problems in artificial intelligence is to achieve successful collaboration across large-scale heterogeneous systems that include Robots, Agents, and People (RAP). In the best case, these RAP systems…
This paper presents an innovative large language model (LLM)-based robotic system for enhancing multi-modal human-robot interaction (HRI). Traditional HRI systems relied on complex designs for intent estimation, reasoning, and behavior…
Heterogeneous multi-robot systems feature significant adaptability for complex environments. However, effective collaboration that fully exploits the robots' potential remains a core challenge. This paper proposes a decentralized…
In disaster scenarios, effective communication is crucial, yet language barriers often hinder timely and accurate information dissemination, exacerbating vulnerabilities and complicating response efforts. This paper presents a novel,…