Contact Transfer: A Direct, User-Driven Method for Human to Robot Transfer of Grasps and Manipulations

Lakshmipathy, Arjun; Bauer, Dominik; Bauer, Cornelia; Pollard, Nancy S.

doi:10.1109/icra46639.2022.9811739

Cited by 6 publications

(1 citation statement)

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“…However, the complexity of data gloves, especially in integrating various sensors, presents significant challenges in terms of cost and maintenance. To simplify the system of human grasping demonstration, the study in [ 22 ] proposed the use of thermal sensors to trace contact areas. With the advent of deep learning technologies, more recent studies have begun to employ simple cameras to track 3D hand poses [ 23 ].…”

Section: Related Workmentioning

confidence: 99%

Robot Grasp Planning: A Learning from Demonstration-Based Approach

Wang,

Fan,

Sakuma

2024

Sensors

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Robot grasping constitutes an essential capability in fulfilling the complexities of advanced industrial operations. This field has been extensively investigated to address a range of practical applications. However, the generation of a stable grasp remains challenging, principally due to the constraints imposed by object geometries and the diverse objectives of the tasks. In this work, we propose a novel learning from demonstration-based grasp-planning framework. This framework is designed to extract crucial human grasp skills, namely the contact region and approach direction, from a single demonstration. Then, it formulates an optimization problem that integrates the extracted skills to generate a stable grasp. Distinct from conventional methods that rely on learning implicit synergies through human demonstration or on mapping the dissimilar kinematics between human hands and robot grippers, our approach focuses on learning the intuitive human intent that involves the potential contact regions and the grasping approach direction. Furthermore, our optimization formulation is capable of identifying the optimal grasp by minimizing the surface fitting error between the demonstrated contact regions on the object and the gripper finger surface and imposing a penalty for any misalignment between the demonstrated and the gripper’s approach directions. A series of experiments is conducted to verify the effectiveness of the proposed algorithm through both simulations and real-world scenarios.

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Section: Related Workmentioning

confidence: 99%