Inspired by the remarkable adaptability observed in biological organisms, multifunctional soft robotics have emerged as promising systems capable of navigating complex environments. In this study, we present a strateg...
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Inspired by the remarkable adaptability observed in biological organisms, multifunctional soft robotics have emerged as promising systems capable of navigating complex environments. In this study, we present a strategy for weaving fiber soft actuators to overcome the existing limitations in deformation capabilities and complex manufacturing processes. This strategy combines traditional rope artistry with the advanced responsive characteristics of electro-driven liquid crystal elastomer (LCE) fibers, facilitating the efficient creation of multifunctional soft actuators. Leveraging this strategy, we have developed four distinct types of soft actuators: the double twisting weaving actuator (DTWA), the circular four-strand weaving actuator (CFWA), the orthogonal weaving actuator (OWA), and the diagonal weaving actuator (DWA). These weaving fiber soft actuators can be readily assembled in various soft robots, granting multiple functionalities, including surface shape programmability, biomimetic blood pumping inspired by the cardiac muscle, and versatile locomotion modes such as crawling and swimming. Our proposed strategy offers unprecedented opportunities for multifunctional soft robots in performing complex tasks.
programming by demonstration is a strategy to simplify the robot programming process for non-experts via human demonstrations. However, its adoption for bimanual tasks is an underexplored problem due to the complexity...
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programming by demonstration is a strategy to simplify the robot programming process for non-experts via human demonstrations. However, its adoption for bimanual tasks is an underexplored problem due to the complexity of hand coordination, which also hinders data recording. This letter presents a novel one-shot method for processing a single RGB video of a bimanual task demonstration to generate an execution plan for a dual-arm robotic system. To detect hand coordination policies, we apply Shannon's information theory to analyze the information flow between scene elements and leverage scene graph properties. The generated plan is a modular behavior tree that assumes different structures based on the desired arms coordination. We validated the effectiveness of this framework through multiple subject video demonstrations, which we collected and made open-source, and exploiting data from an external, publicly available dataset. Comparisons with existing methods revealed significant improvements in generating a centralized execution plan for coordinating two-arm systems.
Imitation Learning (IL) is a promising paradigm for teaching robots to perform novel tasks using demonstrations. Most existing approaches for IL utilize neural networks (NN), however, these methods suffer from several...
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Imitation Learning (IL) is a promising paradigm for teaching robots to perform novel tasks using demonstrations. Most existing approaches for IL utilize neural networks (NN), however, these methods suffer from several well-known limitations: they 1) require large amounts of training data, 2) are hard to interpret, and 3) are hard to refine and adapt. There is an emerging interest in Programmatic Imitation Learning (PIL), which offers significant promise in addressing the above limitations. In PIL, the learned policy is represented in a programming language, making it amenable to interpretation and adaptation to novel settings. However, state-of-the-art PIL algorithms assume access to action labels and struggle to learn from noisy real-world demonstrations. In this paper, we propose Plunder, a novel PIL algorithm that addresses these shortcomings by synthesizing probabilistic programmatic policies that are particularly well-suited for modeling the uncertainties inherent in real-world demonstrations. Our approach leverages an EM loop to simultaneously infer the missing action labels and the most likely probabilistic policy. We benchmark Plunder against several established IL techniques, and demonstrate its superiority across five challenging imitation learning tasks under noise. Plunder policies outperform the next-best baseline by 19% and 17% in matching the given demonstrations and successfully completing the tasks, respectively.
Harmonic potentials provide globally convergent potential fields that are provably free of local minima. Due to its analytical format, it is particularly suitable for generating safe and reliable robot navigation poli...
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Harmonic potentials provide globally convergent potential fields that are provably free of local minima. Due to its analytical format, it is particularly suitable for generating safe and reliable robot navigation policies. However, for complex environments that consist of a large number of overlapping non-sphere obstacles, the computation of associated transformation functions can be tedious. This becomes more apparent when: (i) the workspace is initially unknown and the underlying potential fields are updated constantly as the robot explores it;(ii) the high-level mission consists of sequential navigation tasks among numerous regions, requiring the robot to switch between different potentials. Thus, this work proposes an efficient and automated scheme to construct harmonic potentials incrementally online as guided by the task automaton. A novel two-layer harmonic tree (HT) structure is introduced that facilitates the hybrid combination of oriented search algorithms for task planning and harmonic-based navigation controllers for non-holonomic robots. Both layers are adapted efficiently and jointly during online execution to reflect the actual feasibility and cost of navigation within the updated workspace. Global safety and convergence are ensured both for the high-level task plan and the low-level robot trajectory. Known issues such as oscillation or long-detours for purely potential-based methods and sharp-turns or high computation complexity for purely search-based methods are prevented. Extensive numerical simulation and hardware experiments are conducted against several strong baselines. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Humans exhibit a particular compliant behavior in interactions with their environment. Facilitated by fast physical reasoning, humans are able to rapidly alter their compliance, enhancing robustness and safety in acti...
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Humans exhibit a particular compliant behavior in interactions with their environment. Facilitated by fast physical reasoning, humans are able to rapidly alter their compliance, enhancing robustness and safety in active environments. Transferring these capabilities to robotics is of utmost importance particularly as major space agencies begin investigating the potential of cooperative robotic teams in space. In this scenario, robots in orbit or on planetary surfaces are meant to support astronauts in exploration, maintenance, and habitat building to reduce costs and risks of space missions. A major challenge for interactive robot teams is establishing the capability to act in and interact with dynamic environments. Analogous to humans, the robot should be not only particularly compliant in case of unexpected collisions with other systems but also able to cooperatively handle objects requiring accurate pose estimation and fast trajectory planning. Here, we show that these challenges can be attenuated through an enhancement of active robot compliance introducing a virtual plastic first-order impedance component. We present how elasto-plastic compliance can be realized via energy-based detection of active environments and how evasive motions can be enabled through adaptive plastic compliance. Two space teleoperation experiments using different robotic assets confirm the potential of the method to enhance robustness in interaction with articulated objects and facilitate robot cooperation. An experiment in a health care facility presents how the same method analogously solidifies robotic interactions in human-robot shared environments by giving the robot a subordinate role.
At the RoboCup, a robotics soccer tournament, the Small Size League (SSL) is one of its leagues. The thought of a mechanism in this league that could perform unpredictable kicks and passes inspired study into both the...
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At the RoboCup, a robotics soccer tournament, the Small Size League (SSL) is one of its leagues. The thought of a mechanism in this league that could perform unpredictable kicks and passes inspired study into both the physical mechanism required to do it and the algorithms needed to make the most of *** introducing new ideas and utilizing a Deep Neural Network, this work contributes by improving a prior algorithm that aims to carry out a real-time inversion of the non-linear ordinary differential equation (ODE) that models the ball's path in order to determine the parameters to hit a target with a curved kick mechanism (DNN). New techniques are also presented. The two suggested DNN achieved accuracy levels of more than 92% in the outcomes of simulation runs in MATLAB.
This research explores the impact of two widely used visual programming languages, VPL and Blockly, in educational robotics (ER) to enhance computational skills and address Science, Technology, Engineering, and Mathem...
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This paper presents a new teaching tool with the goal of facilitating the learning of basic programming concepts among high school students and university freshmen. The tool incorporates a user-friendly visual interfa...
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Modern industrial robots can work alongside human workers and coordinate with other robots. This means they can perform complex tasks, but doing so requires complex programming. Therefore, robots are typically program...
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Modern industrial robots can work alongside human workers and coordinate with other robots. This means they can perform complex tasks, but doing so requires complex programming. Therefore, robots are typically programmed by experts, but there are not enough to meet the growing demand for robots. To reduce the need for experts, researchers have tried to make robot programming accessible to factory workers without programming experience. However, none of that previous work supports coordinating multiple robot arms that work on the same task. In this paper we present four block-based programming language designs that enable end-users to program two-armed robots. We analyze the benefits and trade-offs of each design on expressiveness and user cognition, and evaluate the designs based on a survey of 273 professional participants of whom 110 had no previous programming experience. We further present an interactive experiment based on a prototype implementation of the design we deem best. This experiment confirmed that novices can successfully use our prototype to complete realistic robotics tasks. This work contributes to making coordinated programming of robots accessible to end-users. It further explores how visual programming elements can make traditionally challenging programming tasks more beginner-friendly.
Soft robotic grippers with stochastic and topological grasping capabilities can be highly desirable for gentle contact and interaction with fragile objects of various shapes. In this study, we developed soft tentacles...
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