International Journal of Humanoid Robotics

A ballistic walking gait is designed for a planar biped equipped with a wearable walking assist device. The biped is a seven-link planar biped with two legs, two feet, and a trunk. The wearable walking assist device is composed of a bodyweight support, two upper legs, two lower legs, and two shoes. The dynamic model of the biped with its walking assist device, containing two closed kinematic chains, is calculated by virtually cutting each of both loops at one of their point. In the single support phase, the biped with its assist device moves due to the existence of a momentum, produced mechanically, without applying active torques in the inter-link joints. The biped and this assist device are controlled with impulsive torques at the instantaneous double support to obtain a cyclic gait. The impulsive torques are applied in the six inter-link joints of the biped and in several inter-link joints of the wearable walking assist device. The following distributions of impulsive torques, in the knees or the ankles, hips and knees, hips and ankles, or knees and ankles and the fully assist device, are compared with the case of no assistance for the biped. Each time, an infinity of solutions exists to find the impulsive torques. An energy cost functional defined from these impulsive torques is minimized to determine a unique solution. Numerical results show that for a given time period and a given length of the walking gait step, the assistance of the hips is a good compromise to help the biped.

Human-to-humanoid motion retargeting is an important tool to generate human-like humanoid motions. This retargeting problem is often formulated as a Cartesian control problem for the humanoid from a set of task points in the captured human data. Classically, Cartesian control has been developed for redundant systems. While redundancy fundamentally adds new sub-task capabilities, the degree to which secondary objectives can be faithfully executed cannot be determined in advance. In fact, a robot that exhibits redundancy with respect to an operational task may have insufficient degrees of freedom (DOFs) to satisfy more critical constraints. In this paper, we present a Cartesian space resolved acceleration control framework to handle execution of operational tasks and constraints for redundant and nonredundant task specifications. The approach is well suited for online control of humanoid robots from captured human motion data expressed by Cartesian variables. The current formulation enforces kinematic constraints such as joint limits, self-collisions, and foot constraints and incorporates a dynamically-consistent redundancy resolution approach to minimize costly joint motions. The efficacy of the proposed algorithm is demonstrated by simulated and real-time experiments of human motion replication on a Honda humanoid robot model. The algorithm closely tracks all input motions while smoothly and automatically transitioning between regimes where different constraints are binding.

Computational systems for human–robot interaction (HRI) could benefit from visual perceptions of social cues that are commonly employed in human–human interactions. However, existing systems focus on one or two cues for attention or intention estimation. This research investigates how social robots may exploit a wide spectrum of visual cues for multiparty interactions. It is proposed that the vision system for social cue perception should be supported by two dimensions of functionality, namely, vision functionality and cognitive functionality. A vision-based system is proposed for a robot receptionist to embrace both functionalities for multiparty interactions. The module of vision functionality consists of a suite of methods that computationally recognize potential visual cues related to social behavior understanding. The performance of the models is validated by the ground truth annotation dataset. The module of cognitive functionality consists of two computational models that (1) quantify users’ attention saliency and engagement intentions, and (2) facilitate engagement-aware behaviors for the robot to adjust its direction of attention and manage the conversational floor. The performance of the robot’s engagement-aware behaviors is evaluated in a multiparty dialog scenario. The results show that the robot’s engagement-aware behavior based on visual perceptions significantly improve the effectiveness of communication and positively affect user experience.

In this paper, a robotic motion planning scheme is proposed to generate hand-over motions in a friendly and comfortable manner. Two sub-problems are studied: The determination of the final target position (where the robot should hand over the object to) and the generation of human-like motion in the hand-over process (how the robot should hand over the object to the position). First, experiments of different subjects are conducted to explore the common characteristics in the optimal hand-over positions. The result shows that their joint angles are similar at the optimal hand-over position. Therefore, the optimal hand-over position can be determined by forward kinematics based on the measured average joint angles. Then, two methods are proposed to plan the human-like hand-over motions in an obstacle-free environment and in an obstacle-present environment, respectively. A series of experimental comparisons is conducted to prove the validity of the methods. A robotic arm is controlled to hand over a cube to humans in human-like way and nonhuman-like way. The subjects are asked to grade the hand-over motion of the robotic arm on a scale of 1 to 5. The results show that the hand-over motions planned by the methods proposed in this paper can make people feel safer and more comfortable.

This paper studies the problem of achieving an energy efficient walking on an uneven surface for a compass-like biped robot with flat feet. To this end, reference walking gaits, which consist of two phases including the static foot phase and the foot rotation phase, are designed on several typical slopes via minimizing the dimensionless specific mechanical cost of transport. Moreover, for other slopes, a simple transformation method is proposed to generate interim reference walking gaits from the reference ones on those typical slopes. Then an input–output feedback linearization approach is considered to design a tracking control law in the static foot phase and a time-scaling approach is adopted to construct a tracking control law in the foot rotation phase as the dynamics in this phase is underactuated. By switching the control law at different phases and switching the reference walking gaits on different slopes, a compass-like robot can achieve energy efficient walking on uneven surfaces. The validity of our method is illustrated by numerical simulations.

Dance movement is intrinsically connected to the rhythm of music and is a fundamental form of nonverbal communication present in daily human interactions. In order to enable robots to interact with humans in natural real-world environments through dance, these robots must be able to listen to music while robustly tracking the beat of continuous musical stimuli and simultaneously responding to human speech. In this paper, we propose the integration of a real-time beat tracking system with state recovery with different preprocessing solutions used in robot audition for its application to interactive dancing robots. The proposed system is assessed under different real-world acoustic conditions of increasing complexity, which consider multiple audio sources of different kinds, multiple noise sources of different natures, continuous musical and speech stimuli, and the effects of beat-synchronous ego-motion noise and of jittering in ego noise (EN). The overall results suggest improved beat tracking accuracy with lower reaction times to music transitions, while still enhancing automatic speech recognition (ASR) run in parallel in the most challenging conditions. These results corroborate the application of the proposed system for interactive dancing robots.

This paper describes an exploratory study on group interaction with a robot guide in an open large-scale busy environment. For an entire week, a humanoid robot was deployed in the popular Cosmocaixa Science Museum in Barcelona and guided hundreds of people through the museum facilities. The main goal of this experience is to study in the wild the episodes of the robot guiding visitors to a requested destination focusing on the group behavior during displacement. The walking behavior follow-me and the face-to-face communication in a populated environment are analyzed in terms of guide–visitors interaction, grouping patterns and spatial formations. Results from observational data show that the space configurations spontaneously formed by the robot guide and visitors walking together did not always meet the robot communicative and navigational requirements for successful guidance. Therefore, additional verbal and nonverbal prompts must be considered to regulate effectively the walking together and follow-me behaviors. Finally, we discuss lessons learned and recommendations for robot’s spatial behavior in dense crowded scenarios.