Combining Model-based and Learning-based Approaches for Humanoid Grasping at the Towards Robust Grasping and Manipulation Skills for Humanoids workshop
The ability to grasp and manipulate objects provides an essential means to interact with the environment. Recent years have seen a proliferation of research projects to use robotic manipulation in real world applications such as human robot collaboration and industrial tasks. Despite the promising progress, robotic grasping and manipulation has yet to demonstrate necessary robustness and dexterity to be fully exploited in various settings, such as in everyday life contexts, industrial environments, and when dealing with novelty and uncertainty, e.g., object shape, pose, weight, friction at contacts, and with unstructured environments.
Studies on human grasping and manipulation have shown that sensorial capabilities play a key role in the success of human manipulation, allowing a better perception of the object and the interaction with it, and revealing adaptation and control strategies, e.g., using environment and its constraints for more effective manipulation. Inspired by these findings, robotics research aiming to robustify object grasping and manipulation skills shows the importance of effective use of sensory data (visual, tactile, proprioceptive) from planning stage to task completion. Various kinds of approaches have been proposed, e.g., data-driven and empirical approaches such as learning from experience and from human demonstration, analytic approaches such as modelling physical and dynamical constraints manually, and approaches to hand designs such as under-actuated and soft hands.
In this workshop, we aim to bring together researchers and experts in key areas for grasping and manipulation such as perception, control, learning, design of hands and grippers, and studies analysing human manipulation skills. We aspire to identify recent developments in these research areas, both in theory and applications, discussing recent achievements, debating underlying assumptions, and challenges for future progress.