{'fraction_non_alphanumeric': 0.02476780185758514, 'fraction_numerical': 0.0, 'mean_word_length': 5.099056603773585, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Automated Design of Embedded Constraints for Soft Hands Enabling New Grasp Strategies

Soft robotic hands allow to fully exploit hand-object-environment interactions to complete grasping tasks. However, their usability can still be limited in some scenarios (e.g., restricted or cluttered spaces). In this article, we propose to enhance the versatility of soft grippers by adding special passive components to their structure, without completely altering their design, nor their control. A method for the automated design of soft-rigid scoop-shaped add-ons acting as “embedded constraints” is presented. Given a certain gripper and a large set of objects, the design parameters of the optimal scoop for each object are derived by solving an optimization problem. Also the object-environment relative pose is considered in the optimization. The obtained “optimal scoops” are clustered to get a limited set of representative scoop designs which can be prototyped and used in grasping tasks. In this article, we also introduce a data-driven method allowing a grasp planner to select the most suitable scoop to be added to the used hand, given a certain object and its configuration with respect to the surrounding environment. Experiments with two different hands validate the proposed approach.