Among legged robots, hopping and running robots are useful because they can traverse terrain at high speeds and are a benchmark platform for locomotion actuators; if an actuator can power a hopping robot, it can power a walking robot. We aim to create a hopping mechanism for a small-scale, one-legged, untethered hopping robot. A parallel-elastic actuator is an efficient way to do this, and enables the actuator to directly inject energy into the spring, but requires a high-speed, low-inertia actuator. Voice coil actuators are electrically-powered direct-drive translational motors that have very low moving inertia, low friction, can produce force at high speeds, and have a linear force output. These qualities make them ideal candidate motors for a linear elastic actuator in parallel (“LEAP”). Here, we derive an electromechanical model of the LEAP mechanism, develop a simple bang-bang hopping controller, and simulate hopping with a range of spring parameters to find an optimal spring stiffness that maximizes hopping height. We detail our implemented design, and characterize its performance through a series of experiments. We test our robot with different spring stiffnesses, and demonstrate hopping at a maximum steady-state of 3.5 cm ground-clearance (approx. 20% leg length). Our results suggest that the LEAP mechanism may serve the weight-bearing functions of a robot leg.

Walking controllers for bipedal robots have not yet reached human levels of robustness in locomotion. Imitating the human motor control might be an alternative strategy for generating robust locomotion in robots. We seek to control bipedal robots with a specific neuromuscular human walking model proposed previously. Here, we present a virtual neuromuscular controller, VNMC, that emulates this neuromuscular model to generate desired motor torques for a bipedal robot. We test the VNMC on a high-fidelity simulation of the ATRIAS bipedal robot constrained to the sagittal plane. We optimize the control parameters to tolerate maximum ground-height changes, which resulted in ATRIAS walking on a terrain with up to ±7 cm height changes. We further evaluate the robustness of the optimized controller to external and internal disturbances. The optimized VNMC adapts to 90% of random terrains with ground-height changes up to ±2 cm. It endures 95% of ±30 Ns horizontal pushes on the trunk, and 90% of 8 Ns backward and 4 Ns forward impulses on the swing foot throughout the gait cycle. Furthermore, the VNMC is resilient to modeling errors and sensor noise much larger than the equivalent uncertainties in the real robot. The results suggest VNMC as a potential alternative to generate robust locomotion in bipedal robots.