A High-Force Gripper with Embedded Multimodal Sensing for Powerful and Perception Driven Grasping
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arXiv
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| Format: | Preprint |
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2025
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| author | Del Bianco, Edoardo Torielli, Davide Rollo, Federico Gasperini, Damiano Laurenzi, Arturo Baccelliere, Lorenzo Muratore, Luca Roveri, Marco Tsagarakis, Nikos G. |
| author_facet | Del Bianco, Edoardo Torielli, Davide Rollo, Federico Gasperini, Damiano Laurenzi, Arturo Baccelliere, Lorenzo Muratore, Luca Roveri, Marco Tsagarakis, Nikos G. |
| contents | Modern humanoid robots have shown their promising potential for executing various tasks involving the grasping and manipulation of objects using their end-effectors. Nevertheless, in the most of the cases, the grasping and manipulation actions involve low to moderate payload and interaction forces. This is due to limitations often presented by the end-effectors, which can not match their arm-reachable payload, and hence limit the payload that can be grasped and manipulated. In addition, grippers usually do not embed adequate perception in their hardware, and grasping actions are mainly driven by perception sensors installed in the rest of the robot body, frequently affected by occlusions due to the arm motions during the execution of the grasping and manipulation tasks. To address the above, we developed a modular high grasping force gripper equipped with embedded multi-modal perception functionalities. The proposed gripper can generate a grasping force of 110 N in a compact implementation. The high grasping force capability is combined with embedded multi-modal sensing, which includes an eye-in-hand camera, a Time-of-Flight (ToF) distance sensor, an Inertial Measurement Unit (IMU) and an omnidirectional microphone, permitting the implementation of perception-driven grasping functionalities.
We extensively evaluated the grasping force capacity of the gripper by introducing novel payload evaluation metrics that are a function of the robot arm's dynamic motion and gripper thermal states. We also evaluated the embedded multi-modal sensing by performing perception-guided enhanced grasping operations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04970 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A High-Force Gripper with Embedded Multimodal Sensing for Powerful and Perception Driven Grasping Del Bianco, Edoardo Torielli, Davide Rollo, Federico Gasperini, Damiano Laurenzi, Arturo Baccelliere, Lorenzo Muratore, Luca Roveri, Marco Tsagarakis, Nikos G. Robotics Artificial Intelligence Modern humanoid robots have shown their promising potential for executing various tasks involving the grasping and manipulation of objects using their end-effectors. Nevertheless, in the most of the cases, the grasping and manipulation actions involve low to moderate payload and interaction forces. This is due to limitations often presented by the end-effectors, which can not match their arm-reachable payload, and hence limit the payload that can be grasped and manipulated. In addition, grippers usually do not embed adequate perception in their hardware, and grasping actions are mainly driven by perception sensors installed in the rest of the robot body, frequently affected by occlusions due to the arm motions during the execution of the grasping and manipulation tasks. To address the above, we developed a modular high grasping force gripper equipped with embedded multi-modal perception functionalities. The proposed gripper can generate a grasping force of 110 N in a compact implementation. The high grasping force capability is combined with embedded multi-modal sensing, which includes an eye-in-hand camera, a Time-of-Flight (ToF) distance sensor, an Inertial Measurement Unit (IMU) and an omnidirectional microphone, permitting the implementation of perception-driven grasping functionalities. We extensively evaluated the grasping force capacity of the gripper by introducing novel payload evaluation metrics that are a function of the robot arm's dynamic motion and gripper thermal states. We also evaluated the embedded multi-modal sensing by performing perception-guided enhanced grasping operations. |
| title | A High-Force Gripper with Embedded Multimodal Sensing for Powerful and Perception Driven Grasping |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2504.04970 |