State estimation is crucial for legged robots as it directly affects control performance and locomotion stability. In this paper, we propose an Adaptive Invariant Extended Kalman Filter to improve proprioceptive state estimation for legged robots. The proposed method adaptively adjusts the noise level of the contact foot model based on online covariance estimation, leading to improved state estimation under varying contact conditions. It effectively handles small slips that traditional slip rejection fails to address, as overly sensitive slip rejection settings risk causing filter divergence. Our approach employs a contact detection algorithm instead of contact sensors, reducing the reliance on additional hardware. The proposed method is validated through real-world experiments on the quadruped robot LeoQuad, demonstrating enhanced state estimation performance in dynamic locomotion scenarios.

This article proposes a novel evaluation approach on wearing passive upper-limb exoskeletons for overhead tasks in real-world automotive manufacturing lines. We determined that wearing exoskeletons reduced the biomechanical efforts of workers measured by joint kinematics and electromyography as well as the estimated shoulder joint reaction forces and torques derived from simulation. These quantitatively measured variables were statistically associated with subjective measures collected through satisfaction questionnaires. We specifically found that participants increased the shoulder flexion and abduction angles as well as the shoulder range of motion while wearing exoskeletons. Participants also reduced muscle activities, joint torques for shoulder flexion, and reaction forces exerted on the shoulder joints while wearing exoskeletons. Interestingly, our analysis also found that the increased shoulder movement while wearing the device was negatively associated with the satisfaction level. This indicates that although the assistance provided by the device allows users to perform a wider range of arm lifting movements, the deviation from their original movement with the device may lead to decreases in satisfaction levels. This integrative approach using biomechanics and ergonomics suggests that we can potentially predict the subjective scale of satisfaction based on biomechanical variables and preliminarily evaluate the usability and comfort while wearing exoskeletons in real-world settings.

This paper presents a control system platform architecture developed for multi-degrees of freedom (DoFs) robots capable of highly dynamic movements. In robotic applications that require rapid physical interactions with the environment, it is critical for the robot to achieve a high frequency synchronization of data processing from a large number of high-bandwidth actuators and sensors. To address this important problem in robotics, we developed a control system architecture that effectively utilizes the advantages of modern parallel real-time computing technologies: multicore CPU, the Field Programmable Gate Array (FPGA), and distributed local processors. This approach was implemented in the fast running experiments of the MIT Cheetah. In such a highly dynamic robot, the required control bandwidth is particularly high since the MIT Cheetah's leg actuation system is designed to generate high force (output torque up to 100Nm) with high bandwidth (400Hz electrical, 120Hz mechanical) with minimal mechanical impedance for fast locomotive capability. On the integrated control system, a multi-layered architecture is programmed. Inspired by the MapReduce model and the pipelining method, more than 50 processes are operated in parallel, and major processes among them are optimized to achieve the maximum throughput. The proposed architecture enables the control update frequency 4 kHz. With this control system platform, we achieved a high-force proprioceptive impedance control [1], and a trot-running up to 6 m/s with a locomotion efficiency rivaling animals [2]. This control system architecture is well suited for the future trend towards real-time computing system and, thus can be a candidate for a future standard robot control platform.

The MIT Cheetah demonstrated a stable 6 m/s trot gait in the sagittal plane utilizing the self-stable characteristics of locomotion. This paper presents a numerical analysis of the behavior of a quadruped robot model with the proposed controller. We first demonstrate the existence of periodic trot gaits at various speeds and examine local orbital stability of each trajectory using Poincar`e map analysis. Beyond the local stability, we additionally demonstrate the stability of the model against large initial perturbations. Stability of trot gaits at a wide range of speed enables gradual acceleration demonstrated in this paper and a real machine. This simulation study also suggests the upper limit of the command speed that ensures stable steady-state running. As we increase the command speed, we observe series of period-doubling bifurcations, which suggests presence of chaotic dynamics beyond a certain level of command speed. Extension of this simulation analysis will provide useful guidelines for searching control parameters to further improve the system performance.