12 accepted papers
Driven by significant advancements in structure, sensors, and control algorithms, multi-fingered hand systems have received increasing attention from both academia and industry. While existing research primarily focuses on the interaction static rigid objects manipulation, real-world scenarios often
This paper proposes a simple yet effective markerless hand-eye calibration method that achieves low cost, high accuracy, and strong generalization across different types of robots. The method utilizes a circular flange, a standardized structure in industrial robots, for calibration via the perspecti…
Although many impressive advances have been reported in the table tennis robots field using reinforcement learning method, challenges related to policy complexity and adaptability continue to hinder large-scale deployment and practical applications. In this work, building upon extensive prior studie
Table tennis robots have significantly advanced in performance owing to the rapid progress in deep learning and reinforcement learning technologies. However, these advancements often require a large number of training samples. Besides, research focused on the robot serve task remains relatively limi
The tradeoff between the field-of-view and resolution of conventional onboard vision systems primarily results from their fixed optical components. We propose a novel active vision system, Falcon, as an optimal solution. This system comprises an electric zoom lens connected to a high-speed camera wi…
We present One-Shot Affordance Learning (OSAL): a unified pipeline that learns manipulation for articulated objects by observing human demonstration only once. The key idea of our method is to embody affordance of articulated objects with an open-loop trajectory conditioned on a certain area of the…
This letter proposes an online method for calibrating the orientation of cameras mounted on vehicles. To calibrate the orientation of the target camera relative to the vehicle, we propose using a high-speed vision sensor, focused on the ground, in conjunction with the target camera. First, the high-
This work investigates the functionality of Semantic information in Contrastive Learning (SemCL). An advanced pretext task is designed: a contrast is performed between each object and its environment, taken from a scene. This allows the SemCL pretrained model to extract objects from their environmen…
The regrasping function of a robotic hand and arm has been investigated by many studies. Dynamic regrasping is performed by accelerating objects and it has the advantage of being able to perform the regrasp function at high speed. However, the difficulty of increasing the success rate is a persisten…
This paper focuses on high-performance robotic contour tracking under the uncertainties that commonly exist in actual robotic applications. These uncertainties can be attributed to the robot itself (such as modeling errors or mechanical defects like backlash) or to environmental issues (such as cali
In this paper we focus on master-slave robot hand systems that can realize non-contact sensing and intuitive mapping between human hand motion and robot hand motion. Such a master-slave robot hand system can be effective from a viewpoint of usability. However, conventional systems are not able to ad…
Realizing accurate robotic manipulation of flexible objects is very challenging. It asks for dexterous manipulation skill, and demands good adaptation to dynamic uncertainties due to deformation. Unlike traditional methods that basically depend on complex modeling, we follow the opposite approach by…