This paper discusses the implementation of impedance control method to a pair of backdrivable actuators for compliant walking of bipedal robot. The ability of simulating various stiffness of the mechanical joints is validated with experimental measurement. The experiment further shows promising results that the impedance control is able to reduce the ground impact force to protect the hardware.

This paper reports the design, simulation, and experiment of a robotic leg prototype named Cury, which has the potential to achieve minimal clearance and excellent backdrivability. Inspired by human walking data, the actuator design incorporates four-bar linkages and ball screws and is further optimized to meet the torque requirement. The Webots simulation is used to obtain the closed-loop chain description of the robotic leg from fits the URDF specification, and this simulation is used to assess the actuator output requirements at a given predefined trajectory. Leveraging customized ac motors and drives, Cury demonstrates satisfactory trajectory tracking performance using a simple controller. The motor drive design files and Webots simulation files are open-sourced.

Reinforcement learning (RL) techniques have shown great success in many challenging quantitative trading tasks, such as portfolio management and algorithmic trading. Especially, intraday trading is one of the most profitable and risky tasks because of the intraday behaviors of the financial market that reflect billions of rapidly fluctuating capitals. However, a vast majority of existing RL methods focus on the relatively low frequency trading scenarios (e.g., day-level) and fail to capture the fleeting intraday investment opportunities due to two major challenges: 1) how to effectively train profitable RL agents for intraday investment decision-making, which involves high-dimensional fine-grained action space; 2) how to learn meaningful multi-modality market representation to understand the intraday behaviors of the financial market at tick-level.