How Robotic Cues Can Influence Human Decisions: A Study on Consensus Building Using Bias-Controlled Non-linear Opinion Dynamics and Robotic Eye Gaze in Human-Robot Teaming
By leveraging robotic eye gaze as a form of bias in a non-linear opinion dynamics model, robots can influence human decision-making and guide them towards consensus in collaborative tasks.
状況に応じた人間とロボットの共同作業のための継続学習と適応：ボーカルサンドボックス
ボーカルサンドボックスは、人間がロボットに新しい行動を段階的に教えることを可能にすることで、状況に応じた人間とロボットの共同作業を促進するフレームワークである。
Vocal Sandbox: A Framework for Continual Learning and Adaptation in Situated Human-Robot Collaboration Through Multimodal Teaching
Vocal Sandbox enables seamless human-robot collaboration by allowing users to teach robots new behaviors and skills in real-time through spoken dialogue, object keypoints, and kinesthetic demonstrations, leading to more efficient and complex task performance.
PARTNR: A Benchmark for Planning and Reasoning in Human-Robot Collaboration for Household Tasks (Analysis of LLM-Based Agents)
PARTNR, a new benchmark for evaluating human-robot collaboration in household tasks, reveals that while LLMs show promise in planning, they struggle with coordination, error recovery, and real-world perception, highlighting key areas for improvement in embodied AI.
ビジョンベースのヒューマンロボットコラボレーションにおける動的モーションプランニングと実行のためのNMPC-ECBFフレームワーク
本稿では、人間の動きの予測に基づいて安全で効率的な経路計画を実現する、NMPC-ECBFに基づく新しいヒューマンロボットコラボレーション（HRC）制御フレームワークを提案する。
A Framework Combining NMPC and ECBF for Safe and Efficient Dynamic Motion Planning in Vision-Based Human-Robot Collaboration
This paper introduces a novel control framework that leverages Nonlinear Model Predictive Control (NMPC) and Exponential Control Barrier Functions (ECBF) to achieve safe and efficient dynamic motion planning in vision-based human-robot collaboration tasks.
인간-로봇 협업에서 인간의 주도 또는 추종 선호도가 로봇에 대한 인식과 협업에 미치는 영향
인간-로봇 협업에서 작업 계획 및 일정 조정 시 인간의 주도/추종 선호도와 성과를 통합하여 팀 성과를 향상시키고 로봇 및 협업에 대한 긍정적인 인식을 높일 수 있다.
人間主導型とロボット主導型のコラボレーションにおける人間のロボットと協調作業への認識: 好みや作業効率への影響
ロボットとの円滑な共同作業を実現するには、人間の好みや作業効率を考慮したタスク計画と役割分担が重要であり、人間のロボットと協調作業への認識に影響を与える。
Human-Robot Collaboration: Impact of Human Leading/Following Preferences on Task Planning, Performance, and Perception
Integrating human preferences for leading or following into robot task planning and scheduling enhances team performance and human perception of the robot and the collaboration.
Trust-Aware Assistance-Seeking Policy for Robots in Human-Robot Collaboration with Dual-Task Paradigm
By modeling human trust and engagement dynamics, an optimal assistance-seeking policy for robots can be developed to improve overall team performance in collaborative tasks.
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