Model-Based Unified State and Phase Estimation for Torque Actuated Dissipative Spring-Mass Runner Using Limited Sensory Information

In the field of autonomous legged robotics, accurate state estimation is crucial for control and planning. While traditional methods suffice for fully-actuated platforms, under-actuated systems face challenges due to sensory limitations and uncertainties. This paper presents a novel methodology for state estimation and phase prediction, integrating a torque-actuated spring-mass model with limited sensors using a multiple-hypotheses extended Kalman filter. Within this estimation framework, the optimal estimate is determined at each iteration by evaluating the likelihood functions associated with two distinct phase hypotheses, either stance or flight. We evaluate different sensor and motion model combinations, showing that our method achieves precise state and phase estimation even without advanced sensors for compliant and under-actuated platforms.