Authors: Lanpei Li, Enrico Donato, Vincenzo Lomonaco, Egidio Falotico

Published on: April 05, 2024

Impact Score: 7.4

Arxiv code: Arxiv:2404.04219

Summary

• What is new: Introduces Continual Policy Distillation (CPD) framework for versatile control of multi-fingered soft robotic hands.
• Why this is important: Existing control developments for soft robotic hands lack adaptability and generalizability for different objects.
• What the research proposes: CPD framework uses Policy Distillation with exemplar-based rehearsal methods to create adaptive and generalized control policies.
• Results: Achieved versatile and adaptive behaviors in four-fingered soft gripper across various objects, outperforming existing replay strategies.

Technical Details

Technological frameworks used: Continual Policy Distillation (CPD)

Models used: Policy Distillation (PD), expert policy networks, student policy network

Data used: Object manipulation tasks with different shapes and sizes

Potential Impact

Robotics, automation, manufacturing companies; particularly those specializing in logistics, warehousing, and assembly line automation.

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