Related papers: Using an Improved Output Feedback MPC Approach for…
Multimodal simulations augment the presentation of abstract concepts facilitating theoretical models understanding and learning. Most simulations only engage two of our five senses: sight and hearing. If we employ additional sensory…
The robust balancing capability of humanoids is essential for mobility in real environments. Many studies focus on implementing human-inspired ankle, hip, and stepping strategies to achieve human-level balance. In this paper, a robust…
As compared to typical mobile manipulation tasks, sequential mobile manipulation poses a unique challenge -- as the robot operates over extended periods, successful task completion is not solely dependent on consistent motion generation but…
We propose a novel Stochastic Model Predictive Control (MPC) for uncertain linear systems subject to probabilistic constraints. The proposed approach leverages offline learning to extract key features of affine disturbance feedback…
Supporting real-time interactions between human controllers and remote devices remains a challenging goal in the Metaverse due to the stringent requirements on computing workload, communication throughput, and round-trip latency. In this…
A human operator using a manual control interface has ready access to their own command signal, both by efference copy and proprioception. In contrast, a human supervisor typically relies on visual information alone. We propose supplying a…
Model Predictive Control (MPC) is a method to control nonlinear systems with guaranteed stability and constraint satisfaction but suffers from high computation times. Approximate MPC (AMPC) with neural networks (NNs) has emerged to address…
The use of autonomous robots for assistance tasks in hospitals has the potential to free up qualified staff and im-prove patient care. However, the ubiquity of deformable and transparent objects in hospital settings poses signif-icant…
Synaptic Sampling Machine (SSM) is a type of neural network model that considers biological unreliability of the synapses. We propose the circuit design of the SSM neural network which is realized through the memristive-CMOS crossbar…
This paper presents a novel learning-based approach for online estimation of maximal safe sets for local trajectory planning in unknown static environments. The neural representation of a set is used as the terminal set constraint for a…
This paper proposes a model predictive trajectory tracking approach for quadrotors subject to input constraints. Our proposed approach relies on a hierarchical control strategy with an outer-loop feedback generating the required thrust and…
Robotic systems can enhance the amount and repeatability of physically guided motor training. Yet their real-world adoption is limited, partly due to non-intuitive trainer/therapist-trainee/patient interactions. To address this gap, we…
The thesis presents contributions made to the evaluation and design of a haptic guidance system on improving driving performance in cases of normal and degraded visual information, which are based on behavior experiments, modeling and…
Computer simulators are nowadays widely used to understand complex physical systems in many areas such as aerospace, renewable energy, climate modeling, and manufacturing. One fundamental issue in the study of computer simulators is known…
Tactile perception stands as a critical sensory modality for human interaction with the environment. Among various tactile sensor techniques, optical sensor-based approaches have gained traction, notably for producing high-resolution…
Surgical robot simulation platform plays a crucial role in enhancing training efficiency and advancing research on robot learning. Much effort have been made by scholars on developing open-sourced surgical robot simulators to facilitate…
We present a novel Hand-pose Embedding Interactive System (HpEIS) as a virtual sensor, which maps users' flexible hand poses to a two-dimensional visual space using a Variational Autoencoder (VAE) trained on a variety of hand poses. HpEIS…
Machine learning components such as deep neural networks are used extensively in Cyber-Physical Systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for…
In order to optimize the costs and time of design of the new products while improving their quality, concurrent engineering is based on the digital model of these products, the numerical model. However, in order to be able to avoid…
In robotic bimanual teleoperation, multimodal sensory feedback plays a crucial role, providing operators with a more immersive operating experience, reducing cognitive burden, and improving operating efficiency. In this study, we develop an…