SUBTA: A Framework for Supported User-Guided Bimanual Teleoperation in Structured Assembly

In human-robot collaboration, shared autonomy enhances human performance through precise, intuitive support. Effective robotic assistance requires accurately inferring human intentions and understanding task structures to determine optimal support timing and methods. In this paper, we present SUBTA, a supported teleoperation system for bimanual assembly that couples learned intention estimation, scene-graph task planning, and context-dependent motion assists. We validate our approach through a user study (N=12) comparing standard teleoperation, motion-support only, and SUBTA. Linear mixed-effects analysis revealed that SUBTA significantly outperformed standard teleoperation in position accuracy (p<0.001, d=1.18) and orientation accuracy (p<0.001, d=1.75), while reducing mental demand (p=0.002, d=1.34). Post-experiment ratings indicate clearer, more trustworthy visual feedback and predictable interventions in SUBTA. The results demonstrate that SUBTA greatly improves both effectiveness and user experience in teleoperation.

Robot Bed-Making: Deep Transfer Learning Using Depth Sensing of Deformable Fabric

Bed-making is a common task well-suited for home robots since it is tolerant to error and not time-critical. Bed-making can also be difficult for senior citizens and those with limited mobility due to the bending and reaching movements required. Autonomous bed-making combines multiple challenges in robotics: perception in unstructured environments, deformable object manipulation, transfer learning, and sequential decision making. We formalize the bed-making problem as one of maximizing surface coverage with a blanket, and explore algorithmic approaches that use deep learning on depth images to be invariant to the color and pattern of the blankets. We train two networks: one to identify a corner of the blanket and another to determine when to transition to the other side of the bed. Using the first network, the robot grasps at its estimate of the blanket corner and then pulls it to the appropriate corner of the bed frame. The second network estimates if the robot has sufficiently covered one side and can transition to the other, or if it should attempt another grasp from the same side. We evaluate with two robots, the Toyota HSR and the Fetch, and three blankets. Using 2018 and 654 depth images for training the grasp and transition networks respectively, experiments with a quarter-scale twin bed achieve an average of 91.7% blanket coverage, nearly matching human supervisors with 95.0% coverage. Data is available at https://sites.google.com/view/bed-make.