Learning-Based Model Predictive Control (LMPC) is a class of algorithms that enhances Model Predictive Control (MPC) by including machine learning methods, improving robot navigation in complex environments.However, the combination of machine learning and MPC computation in LMPC creates a unique workload that cannot be efficiently handled by a simple GPU and CPU integration.We present HiPER, a hierarchically-composed processing array that provides temporal and spatial mapping capabilities, allowing efficient adaptation to workload changes at runtime.To simplify control, HiPER employs a pointer queue hierarchy to compose and orchestrate program execution.Additionally, HiPER utilizes a fractal interconnect topology that combines local systolic interconnects and their hierarchical extensions to efficiently support the workload's traffic characteristics.To evaluate the performance and efficiency of HiPER, we synthesized a 16.37 mm 2 design in 16nm CMOS.The design consists of 6 pointer queue levels and 1024 PEs.The prototype was assessed using a representative LMPC workload, demonstrating 10.75× improvement in performance compared to a GTX 1080 GPU, 12.80× improvement in energy efficiency compared to a Jetson Orin Nano embedded GPU, and 11.6×/22.2×improvement in performance compared to the RoboX/Plasticine accelerators.

Learning how to walk is a sophisticated neurological task for most animals. In order to walk, the brain must synthesize multiple cortices, neural circuits, and diverse sensory inputs. Some animals, like humans, imitate surrounding individuals to speed up their learning. When humans watch their peers, visual data is processed through a visual cortex in the brain. This complex problem of imitation-based learning forms associations between visual data and muscle actuation through Central Pattern Generation (CPG). Reproducing this imitation phenomenon on low power, energy-constrained robots that are learning to walk remains challenging and unexplored. We propose a bio-inspired feed-forward approach based on neuromorphic computing and event-based vision to address the gait imitation problem. The proposed method trains a "student" hexapod to walk by watching an "expert" hexapod moving its legs. The student processes the flow of Dynamic Vision Sensor (DVS) data with a one-layer Spiking Neural Network (SNN). The SNN of the student successfully imitates the expert within a small convergence time of ten iterations and exhibits energy efficiency at the sub-microjoule level.

Online learning in legged robot under stringent performance and energy constraints thwarts the application of conventional reinforcement learning and optimization algorithms. The integration of complex sensors and data pre-processing required in using these algorithms makes this more challenging. Spiking neural networks allow local learning and low computing power opening new possibilities neuromorphic paradigm to such tasks. Central pattern generation based learning to walk in hexapod robots perfectly matches the temporal learning in SNNs allowing end-to-end learning. We propose a stochastic reinforcement-based algorithm allowing the hexapod to learn using the reward generated by the gyro sensors and camera-based visual inputs. The system is implemented on a Raspberry pi to demonstrate convergence to bio-observed gait patterns.