A Flexible Field-Based Policy Learning Framework for Diverse Robotic Systems and Sensors

We present a cross robot visuomotor learning framework that integrates diffusion policy based control with 3D semantic scene representations from D3Fields to enable category level generalization in manipulation. Its modular design supports diverse robot camera configurations including UR5 arms with Microsoft Azure Kinect arrays and bimanual manipulators with Intel RealSense sensors through a low latency control stack and intuitive teleoperation. A unified configuration layer enables seamless switching between setups for flexible data collection training and evaluation. In a grasp and lift block task the framework achieved an 80 percent success rate after only 100 demonstration episodes demonstrating robust skill transfer between platforms and sensing modalities. This design paves the way for scalable real world studies in cross robotic generalization.

Low-Contact Grasping of Soft Tissue with Complex Geometry using a Vortex Gripper

Soft tissue manipulation is an integral aspect of most surgical procedures; however, the vast majority of surgical graspers used today are made of hard materials, such as metals or hard plastics. Furthermore, these graspers predominately function by pinching tissue between two hard objects as a method for tissue manipulation. As such, the potential to apply too much force during contact, and thus damage tissue, is inherently high. As an alternative approach, gaspers developed using a pneumatic vortex could potentially levitate soft tissue, enabling manipulation with low or even no contact force. In this paper, we present the design and well as a full factorial study of the force characteristics of the vortex gripper grasping soft surfaces with four common shapes, with convex and concave curvature, and ranging over 10 different radii of curvature, for a total of 40 unique surfaces. By changing the parameters of the nozzle elements in the design of the gripper, it was possible to investigate the influence of the mass flow parameters of the vortex gripper on the lifting force for all of these different soft surfaces. An $\pmb{ex}$ $\pmb{vivo}$ experiment was conducted on grasping biological tissues and soft balls of various shapes to show the advantages and disadvantages of the proposed technology. The obtained results allowed us to find limitations in the use of vortex technology and the following stages of its improvement for medical use.

Dexterous Manipulation of Deformable Objects via Pneumatic Gripping: Lifting by One End

Manipulating deformable objects in robotic cells is often costly and not widely accessible. However, the use of localized pneumatic gripping systems can enhance accessibility. Current methods that use pneumatic grippers to handle deformable objects struggle with effective lifting. This paper introduces a method for the dexterous lifting of textile deformable objects from one edge, utilizing a previously developed gripper designed for flexible and porous materials. By precisely adjusting the orientation and position of the gripper during the lifting process, we were able to significantly reduce necessary gripping force and minimize object vibration caused by airflow. This method was tested and validated on four materials with varying mass, friction, and flexibility. The proposed approach facilitates the lifting of deformable objects from a conveyor or automated line, even when only one edge is accessible for grasping. Future work will involve integrating a vision system to optimize the manipulation of deformable objects with more complex shapes.