Abstract
Autonomously exploring the unknown physical properties of novel objects such as stiffness, mass, center of mass, friction coefficient, and shape is crucial for autonomous robotic systems operating continuously in unstructured environments. We introduce a novel visuo-Tactile based predictive cross-modal perception framework where initial visual observations (shape) aid in obtaining an initial prior over the object properties (mass). The initial prior improves the efficiency of the object property estimation, which is autonomously inferred via interactive non-prehensile pushing and using a dual filtering approach. The inferred properties are then used to enhance the predictive capability of the cross-modal function efficiently by using a human-inspired 'surprise' formulation. We evaluated our proposed framework in the real-robotic scenario, demonstrating superior performance.
Original language | English |
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Title of host publication | 2024 IEEE International Symposium on Robotic and Sensors Environments, ROSE 2024 |
Publisher | Institute of Electrical and Electronics Engineers |
Number of pages | 7 |
ISBN (Electronic) | 979-8-3503-6236-7 |
DOIs | |
Publication status | Published - 17 Jul 2024 |
Event | 2024 IEEE International Symposium on Robotic and Sensors Environments, ROSE 2024 - Chemnitz, Germany Duration: 20 Jun 2024 → 21 Jun 2024 |
Conference
Conference | 2024 IEEE International Symposium on Robotic and Sensors Environments, ROSE 2024 |
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Country/Territory | Germany |
City | Chemnitz |
Period | 20/06/24 → 21/06/24 |