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Inspired by the unique neurophysiology of the octopus, a hierarchical framework is proposed that simplifies the coordination of multiple soft arms by decomposing control into high‐level decision‐making, low‐level motor activation, and local reflexive behaviors via sensory feedback. When evaluated in the illustrative problem of a model octopus foraging for food, this hierarchical decomposition results in significant improvements relative to end‐to‐end methods. Performance is achieved through a mixed‐modes approach, whereby qualitatively different tasks are addressed via complementary control schemes. Herein, model‐free reinforcement learning is employed for high‐level decision‐making, while model‐based energy shaping takes care of arm‐level motor execution. To render the pairing computationally tenable, a novel neural network energy shaping (NN‐ES) controller is developed, achieving accurate motions with time‐to‐solutions 200 times faster than previous attempts. The hierarchical framework is then successfully deployed in increasingly challenging foraging scenarios, including an arena littered with obstacles in 3D space, demonstrating the viability of the approach.

 

With the large-scale deployment of connected and autonomous vehicles, the demand on wireless communication spectrum increases rapidly in vehicular networks. Due to increased demand, the allocated spectrum at the 5.9 GHz band for vehicular communication cannot be used efficiently for larger payloads to improve cooperative sensing, safety, and mobility. To achieve higher data rates, the millimeter-wave (mmWave) automotive radar spectrum at 76-81 GHz band can be exploited for communication. However, instead of employing spectral isolation or interference mitigation schemes between communication and radar, we design a joint system for vehicles to perform both functions using the same waveform. In this paper, we propose radar processing methods that use pilots in the orthogonal frequency-division multiplexing (OFDM) waveform. While the radar receiver exploits pilots for sensing, the communication receiver can leverage pilots to estimate the time-varying channel. The simulation results show that proposed radar processing can be efficiently implemented and meet the automotive radar requirements. We also present joint system design problems to find optimal resource allocation between data and pilot subcarriers based on radar estimation accuracy and effective channel capacity.