UCLA

In Study #1, we develop a novel, data-driven method to estimate the kinematic parameters of multi-joint linkages such as the human hand. The method can identify up to two consecutive revolute joint axis orientations between connected rigid bodies. We introduce kinematic constraints into a Generative Topographic Mapping formulation in order to estimate the joint axis parameters. The method was evaluated using simulated motion and via motion capture and a physical 2-DOF mechanism modeled after the metacarpophalangeal joint of the human finger. Our method compares well against state-of-the-art kinematic parameter estimation techniques with regards to reliability and computational efficiency.

In Study #2, we introduce a sensor-embedded soft skin capable of multimodal sensing (contact force and two axes of shear force) in pressurized underwater environments up to 1000kPa. We embed liquid-metal strain gauges within a durable elastomeric skin that is molded around a solid finger core. We demonstrate that the sensor skin is capable of measuring forces up to 220N underwater and while subjected to a range of hydrostatic pressures. We determine that the performance of the sensor skin is unaffected by the submerged, pressurized environment.

In Study #3, we propose a set of design considerations for tactile sensor skins using embedded, microfluidic single-axis strain gauges for the purpose of estimating 3D forces and 1D torque about the skin's surface normal. By displacing shear force taxels such that their principal axes are offset from the point of contact, we are able to more accurately measure torque. We use an experimental testbed to apply force-torque loads to the sensor skin. We develop CNN-based models to evaluate the combined force-torque estimation performance of numerous taxel configurations and provide a detailed discussion of how performance relates to design choices.

In summary, we developed methods that improve sensing in harsh environments such as granular media and underwater. Kinematic and kinetic considerations during hand-object interaction were carefully integrated into the development of novel sensing hardware, data-driven estimation methods, and task-specific sensor design criteria. We advanced the state-of-the-art in tactile sensing using tactile sensor skins and improved the accuracy of hand pose estimation using low-cost motion tracking tools.