Author Correction: Trimmed helicoids: an architectured soft structure yielding soft robots with high precision, large workspace, and compliant interactions
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Trimmed helicoids: an architectured soft structure yielding soft robots with high precision, large workspace, and compliant interactions
The development and use of architectured structures is changing the means by which we design and fabricate soft robots. These materials utilize their topology and geometry to control physical and mechanical structural properties. We propose an architectured structure based on trimmed helicoids that allows for independent regulation of the bending and axial stiffness which facilitates tuneability of the resulting soft robot properties. Leveraging FEA and computational analysis we select a geometry that provides an optimal trade-off between controllability, sensitivity to errors in control, and compliance. By combining these modular trimmed helicoid structures in conjunction with control methods, we demonstrate a meter-scale soft manipulator that shows control precision, large workspace, and compliant interactions with the environment. These properties enable the robot to perform complex tasks that leverage robot-human and robot-environment interactions such as human feeding and collaborative object manipulation.
Categorizing robots by performance fitness into the tree of robots
Robots are typically classified based on specific morphological features, like their kinematic structure. However, a complex interplay between morphology and intelligence shapes how well a robot performs processes. Just as delicate surgical procedures demand high dexterity and tactile precision, manual warehouse or construction work requires strength and endurance. These process requirements necessitate robot systems that provide a level of performance fitting the process. In this work, we introduce the tree of robots as a taxonomy to bridge the gap between morphological classification and process-based performance. It classifies robots based on their fitness to perform, for example, physical interaction processes. Using 11 industrial manipulators, we constructed the first part of the tree of robots based on a carefully deduced set of metrics reflecting fundamental robot capabilities for various industrial physical interaction processes. Through significance analysis, we identified substantial differences between the systems, grouping them via an expectation-maximization algorithm to create a fitness-based robot classification that is open for contributions and accessible.
Discovering dominant dynamics for nonlinear continuum robot control
Continuum robots, which emulate biological organisms’ dexterity and flexibility, hold transformative potential for terrestrial and extraterrestrial applications. While such capabilities present significant modeling and control challenges, these robots’ highly dissipative nature allows their behavior to be explained by low-dimensional, dominant dynamics. Despite extensive research to uncover these dynamics, existing methodologies often fail to produce models that accurately capture them, hindering precise control in diverse and safety-critical tasks. This work addresses this gap by discovering these dynamics and leveraging them in a control methodology that substantially outperforms existing methods. Our approach, grounded in Spectral Submanifold theory, enhances accuracy up to sixfold and improves tracking performance by up to 150 times across a diverse set of control tasks, achieving Pareto dominance in precision and computational efficiency. These advances enable the development of simple yet robust models suitable for real-time control, moving us closer to deploying highly adaptive, efficient, and safe continuum robots.
Physical embodiment and anthropomorphism of AI tutors and their role in student enjoyment and performance
Rising interest in artificial intelligence in education reinforces the demand for evidence-based implementation. This study investigates how tutor agents’ physical embodiment and anthropomorphism (student-reported sociability, animacy, agency, and disturbance) relate to affective (on-task enjoyment) and cognitive (task performance) learning within an intelligent tutoring system (ITS). Data from 56 students (M = 17.75 years, SD = 2.63 years; 30.4% female), working with an emotionally-adaptive version of the ITS “Betty’s Brain”, were analyzed. The ITS’ agents were either depicted as on-screen robots (condition A) or as both on-screen avatars and physical robots (condition B). Physical presence of the tutor agent was not significantly related to task performance or anthropomorphism, but to higher initial on-task enjoyment. Student-reported disturbance was negatively related to initial on-task enjoyment, and student-reported sociability was negatively related to task performance. While physical robots may increase initial on-task enjoyment, students’ perception of certain characteristics may hinder learning, providing implications for designing social robots for education.
Smart insect-computer hybrid robots empowered with enhanced obstacle avoidance capabilities using onboard monocular camera
Insect-computer hybrid robots are receiving increasing attention as a potential alternative to small artificial robots due to their superior locomotion capabilities and low manufacturing costs. Controlling insect-computer hybrid robots to travel through terrain littered with complex obstacles of various shapes and sizes is still challenging. While insects can inherently deal with certain obstacles by using their antennae to detect and avoid obstacles, this ability is limited and can be interfered with by control signals when performing navigation tasks, ultimately leading to the robot being trapped in a specific place and having difficulty escaping. Hybrid robots need to add additional sensors to provide accurate perception and early warning of the external environment to avoid obstacles before getting trapped, ensuring smooth navigation tasks in rough terrain. However, due to insects’ tiny size and limited load capacity, hybrid robots are very limited in the sensors they can carry. A monocular camera is suitable for insect-computer hybrid robots because of its small size, low power consumption, and robust information acquisition capabilities. This paper proposes a navigation algorithm with an integrated obstacle avoidance module using a monocular camera for the insect-computer hybrid robot. The monocular cameras equipped with a monocular depth estimation algorithm based on deep learning can produce depth maps of environmental obstacles. The navigation algorithm generates control commands that can drive the hybrid robot away from obstacles according to the distribution of obstacle distances in the depth map. To ensure the performance of the monocular depth estimation model when applied to insect-computer hybrid robotics scenarios, we collected the first dataset from the viewpoint of a small robot for model training. In addition, we propose a simple but effective depth map processing method to obtain obstacle avoidance commands based on the weighted sum method. The success rate of the navigation experiment is significantly improved from 6.7% to 73.3%. Experimental results show that our navigation algorithm can detect obstacles in advance and guide the hybrid robots to avoid them before they get trapped.
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