相关论文: A Soft e-Textile Sensor for Enhanced Deep Learning…
In essence, successful grasp boils down to correct responses to multiple contact events between fingertips and objects. In most scenarios, tactile sensing is adequate to distinguish contact events. Due to the nature of high dimensionality…
This paper presents a novel approach for robot navigation in environments containing deformable obstacles. By integrating Learning from Demonstration (LfD) with Dynamical Systems (DS), we enable adaptive and efficient navigation in complex…
In robotic manipulation, tactile sensors are indispensable, especially when dealing with soft objects, objects of varying dimensions, or those out of the robot's direct line of sight. Traditional tactile sensors often grapple with…
Advanced machine learning algorithms require platforms that are extremely robust and equipped with rich sensory feedback to handle extensive trial-and-error learning without relying on strong inductive biases. Traditional robotic designs,…
Grasping deformable objects is not well researched due to the complexity in modelling and simulating the dynamic behavior of such objects. However, with the rapid development of physics-based simulators that support soft bodies, the…
Soft tissue simulation in virtual environments is becoming increasingly important for medical applications. However, the high deformability of soft tissue poses significant challenges. Existing methods rely on segmentation, meshing and…
Efficient navigation in dynamic environments is crucial for autonomous robots interacting with moving agents and static obstacles. We present a novel deep reinforcement learning approach that improves robot navigation and interaction with…
Soft pneumatic robot manipulators are popular in industrial and human-interactive applications due to their compliance and flexibility. However, deploying them in real-world scenarios requires advanced sensing for tactile feedback and…
The ability to perform locomotion in various terrains is critical for legged robots. However, the robot has to have a better understanding of the surface it is walking on to perform robust locomotion on different terrains. Animals and…
Continuum and soft robots can transform diverse sectors, including healthcare, agriculture, marine, and space, thanks to their potential to adaptively interact with unstructured environments. These robots exhibit complex mechanics that pose…
Human skin can accurately sense the self-decoupled normal and shear forces when in contact with objects of different sizes. Although there exist many soft and conformable tactile sensors on robotic applications able to decouple the normal…
In this paper, we presented a new method for deformation control of deformable objects, which utilizes both visual and tactile feedback. At present, manipulation of deformable objects is basically formulated by assuming positional…
Compression of soft bodies is central to biology, materials science, and robotics, yet existing contact theories break down at large deformations. Here, we develop a general framework for soft-body compression by extending the method of…
We address the problem of accelerating thin-shell deformable object simulations by dimension reduction. We present a new algorithm to embed a high-dimensional configuration space of deformable objects in a low-dimensional feature space,…
Flexible electrical impedance tomography (EIT) offers a promising alternative to traditional tactile sensing approaches, enabling low-cost, scalable, and deformable sensor designs. Here, we propose an optimized lattice-structured flexible…
Robotic touch, particularly when using soft optical tactile sensors, suffers from distortion caused by motion-dependent shear. The manner in which the sensor contacts a stimulus is entangled with the tactile information about the geometry…
This paper introduces a novel six-axis force/torque sensor tailored for compact and lightweight legged robots. Unlike traditional strain gauge-based sensors, the proposed non-contact design employs photocouplers, enhancing resistance to…
PUBLISHED ON IEEE/ASME TRANSACTIONS ON MECHATRONICS, DOI: 10.1109/TMECH.2021.3100150. Ideally, accurate sensor measurements are needed to achieve a good performance in the closed-loop control of mechatronic systems. As a consequence, sensor…
Deep convolutional neural networks (DCNN for short) are vulnerable to examples with small perturbations. Improving DCNN's robustness is of great significance to the safety-critical applications, such as autonomous driving and industry…
Soft multi-axis force/torque sensors provide safe and precise force interaction. Capturing the complete degree-of-freedom of force is imperative for accurate force measurement with six-axis force/torque sensors. However, cross-axis coupling…