Learning Bipedal Locomotion on Gear-Driven Humanoid Robot Using Foot-Mounted IMUs

Sim-to-real reinforcement learning (RL) for humanoid robots with high-gear ratio actuators remains challenging due to complex actuator dynamics and the absence of torque sensors. To address this, we propose a novel RL framework leveraging foot-mounted inertial measurement units (IMUs). Instead of pursuing detailed actuator modeling and system identification, we utilize foot-mounted IMU measurements to enhance rapid stabilization capabilities over challenging terrains. Additionally, we propose symmetric data augmentation dedicated to the proposed observation space and random network distillation to enhance bipedal locomotion learning over rough terrain. We validate our approach through hardware experiments on a miniature-sized humanoid EVAL-03 over a variety of environments. The experimental results demonstrate that our method improves rapid stabilization capabilities over non-rigid surfaces and sudden environmental transitions.

Development of Bidirectional Series Elastic Actuator with Torsion Coil Spring and Implementation to the Legged Robot

Many studies have been conducted on Series Elastic Actuators (SEA) for robot joints because they are effective in terms of flexibility, safety, and energy efficiency. The ability of SEA to robustly handle unexpected disturbances has raised expectations for practical applications in environments where robots interact with humans. On the other hand, the development and commercialization of small robots for indoor entertainment applications is also actively underway, and it is thought that by using SEA in these robots, dynamic movements such as jumping and running can be realized. In this work, we developed a small and lightweight SEA using coil springs as elastic elements. By devising a method for fixing the coil spring, it is possible to absorb shock and perform highly accurate force measurement in both rotational directions with a simple structure. In addition, to verify the effectiveness of the developed SEA, we created a small single-legged robot with SEA implemented in the three joints of the hip, knee, and ankle, and we conducted a drop test. By adjusting the initial posture and control gain of each joint, we confirmed that flexible landing and continuous hopping are possible with simple PD position control. The measurement results showed that SEA is effective in terms of shock absorption and energy reuse. This work was performed for research purposes only.

Robust Locomotion via Zero-order Stochastic Nonlinear Model Predictive Control with Guard Saltation Matrix

This paper presents a stochastic/robust nonlinear model predictive control (NMPC) to enhance the robustness of legged locomotion against contact uncertainties. We integrate the contact uncertainties into the covariance propagation of stochastic/robust NMPC framework by leveraging the guard saltation matrix and an extended Kalman filter-like covariance update. We achieve fast stochastic/robust NMPC computation by utilizing the zero-order stochastic/robust NMPC algorithm with additional improvements in computational efficiency concerning the feedback gains. We conducted numerical experiments and demonstrate that the proposed method can accurately forecast future state covariance and generate trajectories that satisfies constraints even in the presence of the contact uncertainties. Hardware experiments on the perceptive locomotion of a wheeled-legged robot were also carried out, validating the feasibility of the proposed method in a real-world system with limited on-board computation.