4 accepted papers
The object manipulation is a crucial ability for a service robot, but it is hard to solve with reinforcement learning due to some reasons such as sample efficiency. In this paper, to tackle this object manipulation, we propose a novel framework, AP-NPQL (Non-Parametric Q Learning with Action Primiti…
We propose a novel data-driven learning-based contact clustering (i.e., of contact points and contact normals) framework for rigid-body robot simulation, with its accuracy established/verified by real experimental data. We first construct an experimental robotic setup with force/torque (F/T) sensors…
We present a novel systematic design framework for general under-actuated tendon-driven (UATD) robotic systems to exhibit desired behaviors both during the free motion and the contact task. For this, we propose stiffness decomposition, which enables us to completely decompose the configuration space…
We propose a novel haptic rendering scheme based on passive midpoint integrator (PMI), which is an extension of our prior non-iterative passive mechanical integrator (NPMI) to maximal coordinates for simulation of complex articulated rigid bodies and to multi-point intermittent contact with the line…