For the shape control of deformable free-form surfaces, simulation plays a crucial role in establishing the mapping between the actuation parameters and the deformed shapes. The differentiation of this forward kinematic mapping is usually employed to solve the inverse kinematic problem for determining the actuation parameters that can realize a target shape. However, the free-form surfaces obtained from simulators are always different from the physically deformed shapes due to the errors introduced by hardware and the simplification adopted in physical simulation. To fill the gap, we propose a novel deformation function based sim-to-real learning method that can map the geometric shape of a simulated model into its corresponding shape of the physical model. Unlike the existing sim-to-real learning methods that rely on completely acquired dense markers, our method accommodates sparsely distributed markers and can resiliently use all captured frames -- even for those in the presence of missing markers. To demonstrate its effectiveness, our sim-to-real method has been integrated into a neural network-based computational pipeline designed to tackle the inverse kinematic problem on a pneumatically actuated deformable mannequin.

In this work we propose a learning-based approach to box loco-manipulation for a humanoid robot. This is a particularly challenging problem due to the need for whole-body coordination in order to lift boxes of varying weight, position, and orientation while maintaining balance. To address this challenge, we present a sim-to-real reinforcement learning approach for training general box pickup and carrying skills for the bipedal robot Digit. Our reward functions are designed to produce the desired interactions with the box while also valuing balance and gait quality. We combine the learned skills into a full system for box loco-manipulation to achieve the task of moving boxes from one table to another with a variety of sizes, weights, and initial configurations. In addition to quantitative simulation results, we demonstrate successful sim-to-real transfer on the humanoid r