Hardware Optimization for In-Hand Rotation

Published: 18 Jun 2025, Last Modified: 18 Jun 2025RSS 2025 Hardware Intelligence OralEveryoneRevisionsBibTeXCC BY 4.0
Keywords: Co-design, hardware optimization, manipulation, in-hand
TL;DR: Co-design for in hand rotation via Cross Entropy MPC and Baysian Optimization of hardware parameters
Abstract: In-hand manipulation for robots has recently been possible due to advancements in reinforcement learning and ongoing development of new robotic hands. Both advancements have iteratively pushed the frontiers of manipulation with new controllers allowing more complex manipulators to be effective and hardware advancements allowing true dexterous manip- ulation. Co-design aims to build co-optimized hardware and control systems for complex tasks. This work aims to explore co- design by optimizing hardware of robotic hands. Reinforcement learning has commonly been used as a controller but is often computationally expensive and time consuming to train - making scaling resource intensive across various designs. To explore more effective manipulators at scale, we propose a framework for joint hardware optimization with Cross Entropy MPC for improved sampling efficiency and hardware optimized manipulation.
Submission Number: 6
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