相关论文: Exploring of Discrete and Continuous Input Control…
To rationalize the relatively high investment that industrial automation systems entail, research in the field of intelligent machines should target high value functions such as fettling, die-finishing, deburring, and fixtureless…
Shared autonomy holds promise for improving the usability and accessibility of assistive robotic arms, but current methods often rely on costly expert demonstrations and remain static after pretraining, limiting their ability to handle…
In Physical Human--Robot Interaction (pHRI) grippers, humans and robots may contribute simultaneously to actions, so it is necessary to determine how to combine their commands. Control may be swapped from one to the other within certain…
This paper addresses the challenge of human-guided navigation for mobile collaborative robots under simultaneous proximity regulation and safety constraints. We introduce Adaptive Reinforcement and Model Predictive Control Switching (ARMS),…
With continual advancements in technology, efforts to develop robots simulating human behavior have intensified. Cognitive robotics, combined with artificial intelligence (AI), has proven effective in surveying and research analysis.…
Robotic systems driven by artificial muscles present unique challenges due to the nonlinear dynamics of actuators and the complex designs of mechanical structures. Traditional model-based controllers often struggle to achieve desired…
In robotics, contemporary strategies are learning-based, characterized by a complex black-box nature and a lack of interpretability, which may pose challenges in ensuring stability and safety. To address these issues, we propose integrating…
Active inference, a theoretical construct inspired by brain processing, is a promising alternative to control artificial agents. However, current methods do not yet scale to high-dimensional inputs in continuous control. Here we present a…
Socially assistive robots (SAR) have shown great potential to augment the social and educational development of children with autism spectrum disorders (ASD). As SAR continues to substantiate itself as an effective enhancement to human…
A human-in-the-loop system is proposed to enable collaborative manipulation tasks for person with physical disabilities. Studies show that the cognitive burden of subject reduces with increased autonomy of assistive system. Our framework…
Variable Stiffness Actuators prove invaluable for robotics applications in unstructured environments, fostering safe interactions and enhancing task adaptability. Nevertheless, their mechanical design inevitably results in larger and…
Deep reinforcement learning enables algorithms to learn complex behavior, deal with continuous action spaces and find good strategies in environments with high dimensional state spaces. With deep reinforcement learning being an active area…
Assistive robotic arms often have more degrees-of-freedom than a human teleoperator can control with a low-dimensional input, like a joystick. To overcome this challenge, existing approaches use data-driven methods to learn a mapping from…
Expressivity--the use of multiple modalities to convey internal state and intent of a robot--is critical for interaction. Yet, due to cost, safety, and other constraints, many robots lack high degrees of physical expressivity. This paper…
This paper presents a shared-control rehabilitation policy for a custom 6-degree-of-freedom (6-DoF) upper-limb robot that decomposes complex reaching tasks into decoupled spatial axes. The patient governs the primary reaching direction…
Robot learning is a very promising topic for the future of automation and machine intelligence. Future robots should be able to autonomously acquire skills, learn to represent their environment, and interact with it. While these topics have…
Numerous diseases and aging can cause degeneration of people's balance ability resulting in limited mobility and even high risks of fall. Robotic technologies can provide more intensive rehabilitation exercises or be used as assistive…
We developed a 3D end-effector type of upper limb assistive robot, named as Assistive Robotic Arm Extender (ARAE), that provides transparency movement and adaptive arm support control to achieve home-based therapy and training in the real…
We propose a novel framework for Deep Reinforcement Learning (DRL) in modular robotics using traditional robotic tools that extend state-of-the-art DRL implementations and provide an end-to-end approach which trains a robot directly from…
Mixed Reality (MR) is constantly evolving to inspire new patterns of robot manipulation for more advanced Human- Robot Interaction under the 4th Industrial Revolution Paradigm. Consider that Mixed Reality aims to connect physical and…