Advancements in wearable robots aim to improve user motion, motor control, and overall experience by minimizing energetic cost (EC). However, EC is challenging to measure and it is typically indirectly estimated through respiratory gas analysis. This study introduces a novel EMG-based objective function that captures individuals’ natural energetic expenditure during walking. The objective function combines information from electromyography (EMG) variables such as intensity and muscle synergies. First, we demonstrate the similarity of the proposed objective function, calculated offline, to the EC during walking. Second, we minimize and validate the EMG-based objective function using an online Bayesian optimization algorithm. The walking step frequency is chosen as the parameter to optimize in both offline and online approaches in order to simplify experiments and facilitate comparisons with related research. Compared to existing studies that use EC as the objective function, results demonstrated that the optimization of the presented objective function reduced the number of iterations and, when compared with gradient descent optimization strategies, also reduced convergence time. Moreover, the algorithm effectively converges toward an optimal step frequency near the user’s preferred frequency, positively influencing EC reduction. The good correlation between the estimated objective function and measured EC highlights its consistency and reliability. Thus, the proposed objective function could potentially optimize lower limb exoskeleton assistance and improve user performance and human–robot interaction without the need for challenging respiratory gas measurements.

As the proportion of the elderly population in the USA expands, so will the demand for rehabilitation and social care, which play an important role in maintaining function and mediating motor and cognitive decline in older adults. The use of social robotics and telemedicine are each potential solutions but each have limitations. To address challenges with classical telemedicine for rehabilitation, we propose to use a social robot-augmented telepresence (SRAT), Flo, which was deployed for long-term use in a community-based rehabilitation facility catering to older adults. Our goals were to explore how clinicians and patients would use and respond to the robot during rehab interactions. In this pilot study, three clinicians were recruited and asked to rate usability after receiving training for operating the robot and two of them conducted multiple rehab interactions with their patients using the robot (eleven patients with cognitive impairment and/or motor impairment and 23 rehab sessions delivered via SRAT in total). We report on the experience of both therapists and patients after the interactions.

The language-guided visual robotic grasping task focuses on enabling robots to grasp objects based on human language instructions. However, real-world human-robot collaboration tasks often involve situations with ambiguous language instructions and complex scenarios. These challenges arise in the understanding of linguistic queries, discrimination of key concepts in visual and language information, and generation of executable grasping configurations for the robot’s end-effector. To overcome these challenges, we propose a novel multi-modal transformer-based framework in this study, which assists robots in localizing spatial interactions of objects using text queries and visual sensing. This framework facilitates object grasping in accordance with human instructions. Our developed framework consists of two main components. First, a visual-linguistic transformer encoder is employed to model multi-modal interactions for objects referred to in the text. Second, the framework performs joint spatial localization and grasping. Extensive ablation studies have been conducted on multiple datasets to evaluate the advantages of each component in our model. Additionally, physical experiments have been performed with natural language-driven human-robot interactions on a physical robot to validate the practicality of our approach.

This article describes a novel approach to the control of a powered knee prosthesis where the control system provides passive behavior for most activities and then provides powered assistance only for those activities that require them. The control approach presented here is based on the categorization of knee joint function during activities into four behaviors: resistive stance behavior, active stance behavior, ballistic swing, and non-ballistic swing. The approach is further premised on the assumption that healthy non-perturbed swing-phase is characterized by a ballistic swing motion, and therefore, a replacement of that function should be similarly ballistic. The control system utilizes a six-state finite-state machine, where each state provides different constitutive behaviors (concomitant with the four aforementioned knee behaviors) which are appropriate for a range of activities. Transitions between states and torque control within states is controlled by user motion, such that the control system provides, to the extent possible, knee torque behavior as a reaction to user motion, including for powered behaviors. The control system is demonstrated on a novel device that provides a sufficiently low impedance to enable a strictly passive ballistic swing-phase, while also providing sufficiently high torque to offer powered stance-phase knee-extension during activities such as step-over stair ascent. Experiments employing the knee and control system on an individual with transfemoral amputation are presented that compare the functionality of the power-supplemented nominally passive system with that of a conventional passive microprocessor-controlled knee prosthesis.

The ability to accurately identify human gait intent is a challenge relevant to the success of many applications in robotics, including, but not limited to, assistive devices. Most existing intent identification approaches, however, are either sensor-specific or use a pattern-recognition approach that requires large amounts of training data. This paper introduces a real-time walking speed intent identification algorithm based on the Mahalanobis distance that requires minimal training data. This data efficiency is enabled by making the simplifying assumption that each time step of walking data is independent of all other time steps. The accuracy of the algorithm was analyzed through human-subject experiments that were conducted using controlled walking speed changes on a treadmill. Experimental results confirm that the model used for intent identification converges quickly (within 5 min of training data). On average, the algorithm successfully detected the change in desired walking speed within one gait cycle and had a maximum of 87% accuracy at responding with the correct intent category of speed up, slow down, or no change. The findings also show that the accuracy of the algorithm improves with the magnitude of the speed change, while speed increases were more easily detected than speed decreases.

Our ability to locate moral responsibility is often thought to be a necessary condition for conducting morally permissible medical practice, engaging in a just war, and other high-stakes endeavors. Yet, with increasing reliance upon artificially intelligent systems, we may be facing a widening responsibility gap, which, some argue, cannot be bridged by traditional concepts of responsibility. How then, if at all, can we make use of crucial emerging technologies? According to Colin Allen and Wendell Wallach, the advent of so-called ‘artificial moral agents’ (AMAs) is inevitable. Still, this notion may seem to push back the problem, leaving those who have an interest in developing autonomous technology with a dilemma. We may need to scale-back our efforts at deploying AMAs (or at least maintain human oversight); otherwise, we must rapidly and drastically update our moral and legal norms in a way that ensures responsibility for potentially avoidable harms. This paper invokes contemporary accounts of responsibility in order to show how artificially intelligent systems might be held responsible. Although many theorists are concerned enough to develop artificial conceptions of agency or to exploit our present inability to regulate valuable innovations, the proposal here highlights the importance of—and outlines a plausible foundation for—a workable notion of artificial moral responsibility.

For planning an assembly of a product from a given set of parts, robots necessitate certain cognitive skills: high-level planning is needed to decide the order of actuation actions, while geometric reasoning is needed to check the feasibility of these actions. For collaborative assembly tasks with humans, robots require further cognitive capabilities, such as commonsense reasoning, sensing, and communication skills, not only to cope with the uncertainty caused by incomplete knowledge about the humans’ behaviors but also to ensure safer collaborations. We propose a novel method for collaborative assembly planning under uncertainty, that utilizes hybrid conditional planning extended with commonsense reasoning and a rich set of communication actions for collaborative tasks. Our method is based on answer set programming. We show the applicability of our approach in a real-world assembly domain, where a bi-manual Baxter robot collaborates with a human teammate to assemble furniture.

Although passive occupational exoskeletons alleviate worker physical stresses in demanding postures (e.g., overhead work), they are unsuitable in many other applications because of their lack of flexibility. Active exoskeletons that are able to dynamically adjust the delivered support are required. However, the automatic control of support provided by the exoskeleton is still a largely unsolved challenge in many applications, especially for upper limb occupational exoskeletons, where no practical and reliable approach exists. For this type of exoskeletons, a novel support control approach for lifting and carrying activities is presented here. As an initial step towards a full-fledged automatic support control (ASC), the present article focusses on the functionality of estimating the onset of user’s demand for support. In this way, intuitive behavior should be made possible. The combination of movement and muscle activation signals of the upper limbs is expected to enable high reliability, cost efficiency, and compatibility for use in industrial applications. The functionality consists of two parts: a preprocessing—the motion interpretation—and the support detection itself. Both parts were trained with different subjects, who had to move objects. The functionality was validated both in the cases of (A) an unknown subject performing known tasks and (B) a known subject performing unknown tasks. The functionality showed sound results as it achieved a high accuracy ($$ 95\% $$) in training. In addition, the first validation results showed that this functionality is useful for integration in an appropriately adapted ASC and can then enable comfortable working.

In an era of increased automation in commuters’ everyday life, addressing the disconnect caused by taking the human out of the loop is critical. This paper describes the development of two extreme Companions for a public autonomous bus interface in Singapore. Attributes were identified in a benchmarking study and tested in a survey to investigate local users’ key preferences for must-have and ideal-to-have traits and Companion qualities. Finally, two contrasting Companion concepts are proposed and design strategies considering user expectations of Companions versus humans are discussed.

In human–robot comanipulation, virtual guides are an important tool used to assist the human worker as they constrain the movement of the robot to improve the task accuracy and to avoid undesirable effects, such as collisions with the environment. Consequently, the physical effort and cognitive overload are reduced during accomplishment of comanipulative tasks. However, the construction of virtual guides often requires expert knowledge and modeling of the task, which restricts the usefulness of virtual guides to scenarios with fixed constraints. Moreover, few approaches have addressed the implementation of virtual guides enforcing orientation constraints and, when done, these approaches have treated translation and orientation separately, and consequently there is no synchronization of the translational and rotational motions. To overcome these challenges and enhance the programming flexibility of virtual guides, we present a new framework that allows the user to create 6D virtual guides through XSplines which we define as a combination of Akima splines for the translation component and spherical cubic interpolation of quaternions for the orientation component. For complex tasks, the user is able to initially define a 3D virtual guide and then use this assistance in translational motion to concentrate only on defining the orientations along the path. It is also possible for the user to modify a particular point or portion of a guide while being assisted by it. We demonstrate in an industrial scenario that these innovations provide an intuitive solution to extend the use of virtual guides to 6 degrees of freedom and increase the human worker’s comfort during the programming phase of these guides in an assisted human–robot comanipulation context.

This paper presents a non-model-based collision detection algorithm for robots without external sensors and with closed control architecture. A reference signal of repetitive motion is recorded from the robot operation. To detect collisions, the reference is compared with measurements from the robot. One of the key contributions is a novel approach to optimal matching of compared signals, which is ensured by the newly developed modified Dynamic Time Warping (mDTW) method presented in this paper. One of the main improvements of the mDTW is that it enables comparing a signal with the most similar section of the other signal. Partial matching also enables online application of time warping principles and reduces the time and computation resources needed to perform matching. In addition to mDTW, two complementary decision rules are developed to identify collisions. The first rule, based on the absolute difference between compared matched samples, uses statistically determined thresholds to perform rapid detection of unambiguous collisions. The second rule is based on Eigen values of the covariance matrix of matched samples, and it employs its higher sensitivity to detect collisions with lower intensity. Results from experimental validation of the proposed collision algorithm on two industrial robots are shown.

Robotic devices are well-suited to provide high intensity upper limb therapy in order to induce plasticity and facilitate recovery from brain and spinal cord injury. In order to realise gains in functional independence, devices that target the distal joints of the arm are necessary. Further, the robotic device must exhibit key dynamic properties that enable both high dynamic transparency for assessment, and implementation of novel interaction control modes that significantly engage the participant. In this paper, we present the kinematic design, dynamical characterization, and clinical validation of the RiceWrist-S, a serial robotic mechanism that facilitates rehabilitation of the forearm in pronation-supination, and of the wrist in flexion-extension and radial-ulnar deviation. The RiceWrist-Grip, a grip force sensing handle, is shown to provide grip force measurements that correlate well with those acquired from a hand dynamometer. Clinical validation via a single case study of incomplete spinal cord injury rehabilitation for an individual with injury at the C3-5 level showed moderate gains in clinical outcome measures. Robotic measures of movement smoothness also captured gains, supporting our hypothesis that intensive upper limb rehabilitation with the RiceWrist-S would show beneficial outcomes.