8 accepted papers
Real-world contact-rich manipulation demands robots to perceive temporal tactile feedback, capture subtle surface deformations, and reason about object properties and force dynamics. Although optical tactile sensors are uniquely capable of providing such rich information, existing tactile datasets a…
This article presents a novel vision-based artificial lateral line (ALL) sensor, FlowSight, enhancing the perception capabilities of underwater robots. Through an autonomous vision system, FlowSight allows for simultaneous sensing the speed and direction of local water flow without relying on extern…
Visuotactile sensors can provide rich contact information for robots. However, how to build a high-fidelity visuotactile simulator that supports multi-mode tactile imprints and various sensor configurations remains a challenging problem. In this paper, we present TacFlex, a flexible simulator for vi…
To complete tasks in dynamic environments, robots need to timely update their plans to react to environment changes. Traditional stripe-like or learning-based planners struggle to achieve this due to their high reliance on meticulously predefined planning rules or labeled data. Fortunately, recent w
Visuotactile sensors have recently attracted much attention in robot communities due to the benefit of high spatial resolution sensing. However, force/torque estimation by visuotactile sensors remains a challenging problem. In this paper, we propose a learning-based six-axis force/torque estimation…
Adapting the mastered manipulation skill to novel objects is still challenging for robots. Recent works have attempted to endow the robot with the ability to adapt to unseen tasks by leveraging meta-learning. However, these methods are data-hungry in the training phase, which limits their applicatio
Humans can quickly determine the force required to grasp a deformable object to prevent its sliding or excessive deformation through vision and touch, which is still a challenging task for robots. To address this issue, we propose a novel 3D convolution-based visual-tactile fusion deep neural networ…
Predicting whether a particular grasp will succeed is critical to performing stable grasping and manipulating tasks. Robots need to combine vision and touch as humans do to accomplish this prediction. The primary problem to be solved in this process is how to learn effective visual-tactile fusion fe