Imitation of Arm Gestures by the Semi-Humanoid Robot NICO

Articulated Humanoid Head for a Robot Receptionist Capable of Natural Human Interaction

Humanoid robots have become increasingly popular in applications such as social interaction, education, and service roles, which drives the need for more natural and efficient human-robot interactions. However, currently available humanoid heads often face limitations, including high costs, mechanical complexity, and limited adaptability across diverse environments. To address these challenges, we present an articulated humanoid robot head designed for a receptionist role, integrating a mechanical structure with 21 degrees of freedom (DoF), including mechanisms for the mouth, eyes, eyebrows, and neck, and covered with realistic silicone skin to achieve a human-like appearance and expression. The system integrates a model-based architecture that combines SCRFD, ArcFace, and ByTetrack for face recognition and Llama and Whisper for natural language processing, with hardware support enabling real-time operations and human re-identification. The conversational ability and re-identification capabilities of the humanoid robot head were quantitatively measured, while its emotional expressiveness and human likeness were evaluated through a user study, achieving an average human likeness score of 4.13 out of 5.

Optimization of sim-to-real transfer in the humanoid robot NICO

This work added YOLO-based object and hand detection, stereo vision-based localization using the robot's built-in low-resolution fisheye cameras, and task-specific corrections for grasp execution to form a novel calibration-based grasping pipeline that does not require RGB-D cameras, motion capture, or external tracking systems.

Robot Learning from Human Demonstrations: Handwritten Alphabet Trajectories and Human-Likeness Evaluation

A framework for learning human-like robot motion from demonstration, including data collection, probabilistic trajectory learning, and perceptual user evaluation is presented, extending the widely used Gaussian Mixture Model and Gaussian Mixture Regression approach for learning from demonstration.

Human Hand Kinematic Models for Sensor Glove-Based HMI: A Review

Abstract The human hand contains 27 bones, 36 articulations, 39 active muscles, and is commonly represented by approximately 20–27 functional degrees of freedom, depending on the adopted kinematic model. Accurate reconstruction of hand motion is essential for sensor glove-based human–machine interfaces (HMIs) used in robotics, rehabilitation, teleoperation, virtual and augmented reality, prosthetics, and gesture-based interaction. This review analyzes human hand kinematic models and their integration with sensor glove technologies for motion capture and hand pose reconstruction. Rigid-body, musculoskeletal, data-driven, and hybrid modeling approaches are compared with respect to anatomical fidelity, computational complexity, and suitability for real-time applications. The review also examines sensing technologies, including flex sensors, inertial measurement units, optical tracking systems, and multimodal sensing architectures, together with calibration and sensor fusion methods. The reviewed literature indicates that rigid-body models remain the most widely used approach because of their computational efficiency and compatibility with wearable sensing systems, whereas musculoskeletal models provide greater anatomical realism at higher computational cost. The review identifies hybrid modeling, standardized calibration procedures, multimodal sensor fusion, and benchmark datasets for hand motion reconstruction as key directions for future research in sensor glove-based HMIs.

Visual Safe Human-to-Humanoid Motion Imitation

Safe human-to-humanoid motion imitation is crucial for shared environments, where direct motion retargeting may induce self-collision or human–humanoid collision due to embodiment mismatch, kinematic limits, perception uncertainty, and human proximity. This paper presents an online vision-aided safe human-to-humanoid motion imitation framework that integrates skeleton-based upper-body pose estimation, joint-space retargeting, and capsule-based Control Barrier Function Quadratic Program (CBF-QP) safety filtering. Human skeletal observations are mapped to a reduced eight-degree-of-freedom (8-DoF) humanoid upper-body command, while the CBF-QP layer computes a safety-corrected target that minimally modifies the nominal imitation command subject to robot self-collision and human–humanoid collision constraints. The framework is evaluated via simulation and hardware experiments under representative self-collision and human–humanoid interaction episodes, complemented by a comparative benchmark against velocity damping and potential field baselines. Furthermore, this work introduces an evaluation protocol combining geometric safety, command deviation, and local-link similarity metrics to systematically characterize the safety–imitation trade-off governed by CBF parameters. The results demonstrate that, within the tested moderate-speed regime, the proposed framework substantially reduces geometric collision violations while balancing imitation fidelity with online computational feasibility, thereby providing a viable foundation for safe human-guided humanoid motion deployment.

Emotional Motion Design and Evaluation of Humanoid Robot Based on PAD Model