The physical core of bipedal locomotion is the continuous mapping of center-of-mass momentum and ground reaction forces against gravity. Traditional control frameworks isolate specific gaits through predefined scheduling or rigid action mimicry, aggressively interrupting smooth multi-body dynamics. The GaitSpan framework constructs a unified state-action mapping space to resolve this discontinuity. Using a baseline walking policy, the algorithm executes parameterized evolution to directly generate torque commands for high-energy states. The control system bypasses zero-start trial-and-error and conforms to hardware physical constraints, achieving continuous motion generation under impact transitions from the low-frequency to the high-frequency domain.
The architecture operates on a model-free reinforcement learning mechanism. The input layer ingests Inertial Measurement Unit (IMU) and joint encoder data, while the output layer generates target commands for low-level Proportional-Derivative (PD) controllers. A rigorous engineering audit reveals a severe lack of critical deployment parameters in the theoretical abstraction. The disclosure omits specific hardware models for Sim2Real validation, the exact control loop execution frequency, and the weight distribution structure within the reward function. Empirical Cost of Transport (CoT) metrics during real-world physical testing remain completely undocumented. The absence of these parameters leaves the energetic viability of the framework unverified for physical deployment.
Software algorithmic generalization frequently conceals the hard constraints of physical hardware fatigue. The phase transition from walking into running generates transient ground reaction forces exceeding three times the system's overall body weight. When subjected to these high-frequency alternating loads, the harmonic strain wave gears dominating the current mass-production supply chain are highly susceptible to plastic deformation and flexspline fatigue fracture. Forcing a continuous phase-transition gait on existing hardware triggers an exponential degradation in motor thermal dissipation and transmission lifespan. The lack of high-burst, low-cost actuators directly paralyzes the scalable deployment of this algorithm across consumer or industrial-grade humanoid robots, proving that advanced locomotion policies cannot outpace mechanical yield limits.