Recent researches on robotics have shown significant improvement, spanning from algorithms, mechanics to hardware architectures. Robotics, including manipulators, legged robots, drones, and autonomous vehicles, are now widely applied in diverse scenarios. However, the high computation and data complexity of robotic algorithms pose great challenges to its applications. On the one hand, CPU platform is flexible to handle multiple robotic tasks. GPU platform has higher computational capacities and easy-touse development frameworks, so they have been widely adopted in several applications. On the other hand, FPGA-based robotic accelerators are becoming increasingly competitive alternatives, especially in latency-critical and power-limited scenarios. With specialized designed hardware logic and algorithm kernels, FPGA-based accelerators can surpass CPU and GPU in performance and energy efficiency. In this paper, we give an overview of previous work on FPGA-based robotic accelerators covering different stages of the robotic system pipeline. An analysis of software and hardware optimization techniques and main technical issues is presented, along with some commercial and space applications, to serve as a guide for future work. Therefore, the computation and storage complexity, as well as real-time and power constraints of the robotic system, Over the last decade, we have seen significant progress hinders its wide application in latency-critical or power-limited in the development of robotics, spanning from algorithms, scenarios . Various robotic systems, like Therefore, it is essential to choose a proper compute platform manipulators, legged robots, unmanned aerial vehicles, selfdriving for the robotic system. CPU and GPU are two widely cars have been designed for search and rescue , , used commercial compute platforms. CPU is designed to exploration , , package delivery , entertainment , handle a wide range of tasks quickly and is often used to  and more applications and scenarios. These robots are develop novel algorithms. A typical CPU can achieve 10-on the rise of demonstrating their full potential. Take drones, 100 GFLOPS with below 1GOP/J power efficiency . In a type of aerial robots, for example, the number of drones contrast, GPU is designed with thousands of processor cores has grown by 2.83x between 2015 and 2019 based on the running simultaneously, which enable massive parallelism. The typical GPU can perform up to 10 TOPS performance and registered number has reached 1.32 million in 2019, and the become a good candidate for high-performance scenarios. Recently, FFA expects this number will come to 1.59 billion by 2024.