State Estimation for Compliant and Morphologically Adaptive Robots

Abstract-- Locomotion robots with active or passive compliance can show robustness to uncertain scenarios, which can be promising for agricultural, research and environmental industries. However, state estimation for these robots is challenging due to the lack of rigid-body assumptions and kinematic changes from morphing. We propose a method to estimate typical rigid-body states alongside compliance-related states, such as soft robot shape in different morphologies and locomotion modes. Our neural network-based state estimator uses a history of states and a mechanism to directly influence unreliable sensors. We test our framework on the GOA T platform, a robot capable of passive compliance and active morphing for extreme outdoor terrain. The network is trained on motion capture data in a novel compliance-centric frame that accounts for morphing-related states. Our method predicts shape-related measurements within 4.2% of the robot's size, velocities within 6.3% and 2.4% of the top linear and angular speeds, respectively, and orientation within 1.5 For robots to support outdoor industries and research activities, such as animal monitoring, climate surveillance, and agriculture, they require the capability to operate within and traverse extreme terrain conditions [1][2][3]. While animals demonstrate such capabilities, and can operate on challenging and varied terrains, typical state-of-the art robotic solutions, such as rigid-body quadrupeds, are primarily confined to challenging but man-made terrain [4]. This can be partially attributed to their reliance on a singular mode of locomotion and an inability to physically adapt to the wide range of conditions found in outdoor environments [5][6].