🤖 AI Summary
This study addresses the challenge of achieving efficient, low-power RGB-D affordance segmentation for wearable robots on embedded platforms. The authors propose two key innovations: first, a hardware-aware neural architecture search space specifically designed for depth-informed fusion; and second, a lightweight preprocessing layer that aligns depth maps with RGB inputs, enabling seamless integration with existing lightweight networks and a tailored fine-tuning strategy. As the first work to systematically explore RGB-D fusion for embedded affordance segmentation, this research achieves Pareto-optimal trade-offs between performance and power consumption on a Jetson Nano platform paired with a RealSense camera. The approach significantly outperforms current lightweight methods on real-world datasets and enables real-time operation under battery power.
📝 Abstract
While depth sensors have the potential to complement RGB data for affordance segmentation in wearable robots, their usage seems to remain underexplored. The paper proposes two approaches: a reformulated version of hardware-aware neural architecture search, endowed with a newly designed search space to integrate depth (D) information into small-sized deep networks, and a dedicated fine-tuning approach, including a preprocessing layer to merge depth information with RGB data and make it compatible with conventional architectures. In both cases, those methods aim to generate solutions that benefit from modern (portable) hardware accelerators and overcome existing tiny-like approaches, which often fail to tackle critical scenarios due to the severe constraints set by the supporting hardware. Extensive experiments on a pair of real-world datasets demonstrate the effectiveness of the proposed method as compared with existing solutions. The approach presented in the paper generates, in most cases, solutions that identify the Pareto optimal front to balance generalization performance and hardware requirements. The paper also describes the supporting prototype, including a Jetson Nano board and a RealSense RGB-D camera. When considering the energy profile of the device, the overall system can attain real-time performances within an energy budget that is compatible with standard batteries, such as those used in smartphones.