Locomotion and manipulation are two essential skills in robotics but are often divided or decoupled into two separate problems. It is widely accepted that the topological duality between multi-legged locomotion and multi-fingered manipulation shares an intrinsic model. However, a lack of research remains to identify the data-driven evidence for further research. This paper explores a unified formulation of the loco-manipulation problem using reinforcement learning (RL) by reconfiguring robotic limbs with an overconstrained design into multi-legged and multi-fingered robots. Such design reconfiguration allows for adopting a co-training architecture for reinforcement learning towards a unified loco-manipulation policy. As a result, we find data-driven evidence to support the transferability between locomotion and manipulation skills using a single RL policy with a multilayer perceptron or graph neural network. We also demonstrate the Sim2Real transfer of the learned loco-manipulation skills in a robotic prototype. This work expands the knowledge frontiers on loco-manipulation transferability with learning-based evidence applied in a novel platform with overconstrained robotic limbs.
The increasing devices and their demand for network force the IoT network for smart grids to face the great challenges in privacy, security, trust, etc. In this paper, we apply blockchain to IoT communication network supported by WiFi. We first analyze the benefit that can be obtained by utilizing blockchain. And Secondly, we propose a blockchain enabled IoT network architure, within which, WAPI is applied as the authentication standard while blockchain is used as the trust guarantee mechanism. Thridly, we analyze how to guarantee the security, privacy, resource management within the proposed architecture. Finally, we conclude our work.
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