Dexterous grasping of unseen objects in dynamic
environments is an essential prerequisite for the advanced
manipulation of autonomous robots. Prior advances rely on
several assumptions that simplify the setup, including environ-
ment stationarity, pre-defined objects, and low-dimensional end-
effectors. Though easing the problem and enabling progress, it
undermined the complexity of the real world. Aiming to relax
these assumptions, we present a dynamic grasping framework
for unknown objects in this work, which uses a five-fingered
hand with visual servo control and can compensate for external
disturbances. To establish such a system on real hardware, we
leverage the recent advances in real-time dexterous generative
grasp synthesis and introduce several techniques to secure
the robustness and performance of the overall system. Our
experiments on real hardware verify the ability of the proposed
system to reliably grasp unknown dynamic objects in two
realistic scenarios: objects on a conveyor belt and human-robot
handover. Note that there has been no prior work that can
achieve dynamic multi-fingered grasping for unknown objects
like ours up to the time of writing this paper. We hope our
pioneering work in this direction can provide inspiration to
the community and pave the way for further algorithmic and
engineering advances on this challenging task. A video of the
experiments is available at https://youtu.be/b87zGNoKELg.
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Dexterous grasping of unseen objects in dynamic
environments is an essential prerequisite for the advanced
manipulation of autonomous robots. Prior advances rely on
several assumptions that simplify the setup, including environ-
ment stationarity, pre-defined objects, and low-dimensional end-
effectors. Though easing the problem and enabling progress, it
undermined the complexity of the real world. Aiming to relax
these assumptions, we present a dynamic grasping framework
for unknown obj...
»