7 accepted papers
Accurate depth maps are essential for robotic grasping. However, transparent objects often cause depth cameras to produce missing or distorted depth due to reflection and refraction, making grasping them particularly challenging. Precise depth estimation for transparent objects is therefore crucial.
In this letter, we present a high-rate and robust multi-sensor fusion framework for state estimation of humanoid robots, named HR<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>-KIL
In object manipulation, movements are inherently restricted by object geometry and dynamics. Humans use an intuitive understanding of physics while grasping objects, resulting in an efficient application of manipulation force. This involves a ‘common sense’ awareness of how objects behave in the phy
Recently, reinforcement learning (RL) is often used for learning the strategy of peg-in-hole tasks. However, traditional state representation of PiH RL might be either redundant or abstract, which leads to unnecessary learning steps and incompatibility with mathematical training optimization. To iss
A robot in the future may initially has a good learning capability but an empty library of movements. It gradually enriches its library of movements through human demonstrations. Dynamic Movement Primitives (DMPs) has been proved to be an effective way to represent trajectories. Trajectories are cla…
To actively assist human walking and balance recovery, a unified active assistance control framework of the hip exoskeleton is proposed in this paper. At the beginning of this paper, the condition of active assistance is analyzed. And then, a novel virtual stiffness model is proposed based on the an…