GOTT is a new framework that improves the ability of multi-fingered robots to manipulate objects by establishing reliable contact. It adapts to various tasks and robot designs, showing enhanced success in both simulations and real-world applications.
This paper introduces a kinematic framework that connects serial and parallel mechanisms, enhancing robotic motion analysis. Validation through various mechanisms shows high accuracy in results.
This research introduces a new framework for robotic exploration that balances the need for information gain with the risks involved in hazardous environments. The findings suggest that robots can effectively reason about the trade-offs between uncertainty reduction and risk exposure.
A new approach to multi-robot informative path planning utilizes a grid-based GP-Kalman filtering framework. This method improves target monitoring by addressing spatial uncertainty and communication challenges, showing promising results in simulations and real-world tests.
This research presents a novel framework for improving failure detection in vision-language-action robotic systems. By leveraging unlabeled data and active learning, the method enhances detection accuracy during manipulation tasks.