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While current humanoid whole-body control frameworks predominantly rely on the static environment assumptions, addressing tasks characterized by high dynamism and complex interactions presents a formidable challenge. In this paper, we…
Adaptation to external and internal changes is major for robotic systems in uncertain environments. Here we present a novel multisensory active inference torque controller for industrial arms that shows how prediction can be used to resolve…
In human-robot collaboration (HRC), robots must adapt online to dynamic task constraints and evolving human intent. While physical corrections provide a natural, low-latency channel for operators to convey motion-level adjustments,…
The rising number of the elderly incurs growing concern about healthcare, and in particular rehabilitation healthcare. Assistive technology and assistive robotics in particular may help to improve this process. We develop a robot coach…
Planning and execution of agile locomotion maneuvers have been a longstanding challenge in legged robotics. It requires to derive motion plans and local feedback policies in real-time to handle the nonholonomy of the kinetic momenta. To…
Bilateral teleoperation provides humanoid robots with human planning intelligence while enabling the human to feel what the robot feels. It has the potential to transform physically capable humanoid robots into dynamically intelligent ones.…
Prior to realizing fully autonomous driving, human intervention will be required periodically to guarantee vehicle safety. This fact poses a new challenge in human-machine interaction, particularly during control authority transition from…
Impedance-based control represents a prevalent strategy in the powered trans femoral prostheses because of its ability to reproduce natural walking. However, most existing studies have developed impedance-based prosthesis controllers for…
Despite the impressive advancements achieved using deep-learning for functional brain activity analysis, the heterogeneity of functional patterns and scarcity of imaging data still pose challenges in tasks such as prediction of future onset…
Prognoses of Traumatic Brain Injury (TBI) outcomes are neither easily nor accurately determined from clinical indicators. This is due in part to the heterogeneity of damage inflicted to the brain, ultimately resulting in diverse and complex…
Gait phase estimation based on inertial measurement unit (IMU) signals facilitates precise adaptation of exoskeletons to individual gait variations. However, challenges remain in achieving high accuracy and robustness, particularly during…
This paper presents an Impedance Primitive-augmented hierarchical reinforcement learning framework for efficient robotic manipulation in sequential contact tasks. We leverage this hierarchical structure to sequentially execute behavior…
A typical application of upper-limb exoskeleton robots is deployment in rehabilitation training, helping patients to regain manipulative abilities. However, as the patient is not always capable of following the robot, safety issues may…
Mobile manipulators are designed to perform complex sequences of navigation and manipulation tasks in human-centered environments. While recent optimization-based methods such as Hierarchical Task Model Predictive Control (HTMPC) enable…
This work presents an extended framework for learning-based bipedal locomotion that incorporates a heuristic step-planning strategy guided by desired torso velocity tracking. The framework enables precise interaction between a humanoid…
Due to the several applications on Human-machine interaction (HMI), this area of research has become one of the most popular in recent years. This is the case for instance of advanced training machines, robots for rehabilitation, robotic…
Physical movement therapy is a crucial method of rehabilitation aimed at reinstating mobility among patients facing motor dysfunction due to neurological conditions or accidents. Such therapy is usually featured as patient-specific,…
This paper proposes a hybrid learning and optimization framework for mobile manipulators for complex and physically interactive tasks. The framework exploits an admittance-type physical interface to obtain intuitive and simplified human…
Intelligent wearable systems are at the forefront of precision medicine and play a crucial role in enhancing human-machine interaction. Traditional devices often encounter limitations due to their dependence on empirical material design and…
With recent advancements in AI and computational tools, intelligent paradigms have emerged to enhance fields like shared autonomy and human-machine teaming in healthcare. Advanced AI algorithms (e.g., reinforcement learning) can…