Hybrid Gripper with Passive Pneumatic Soft Joints for Grasping Deformable Thin Objects

Grasping a variety of objects remains a key challenge in the development of versatile robotic systems. The human hand is remarkably dexterous, capable of grasping and manipulating objects with diverse shapes, mechanical properties, and textures. Inspired by how humans use two fingers to pick up thin and large objects such as fabric or sheets of paper, we aim to develop a gripper optimized for grasping such deformable objects. Observing how the soft and flexible fingertip joints of the hand approach and grasp thin materials, a hybrid gripper design that incorporates both soft and rigid components was proposed. The gripper utilizes a soft pneumatic ring wrapped around a rigid revolute joint to create a flexible two-fingered gripper. Experiments were conducted to characterize and evaluate the gripper performance in handling sheets of paper and other objects. Compared to rigid grippers, the proposed design improves grasping efficiency and reduces the gripping distance by up to eightfold.

Single Actuator Undulation Soft-bodied Robots Using A Precompressed Variable Thickness Flexible Beam

Soft robots - due to their intrinsic flexibility of the body - can adaptively navigate unstructured environments. One of the most popular locomotion gaits that has been implemented in soft robots is undulation. The undulation motion in soft robots resembles the locomotion gait of stringy creatures such as snakes, eels, and C. Elegans. Typically, the implementation of undulation locomotion on a soft robot requires many actuators to control each segment of the stringy body. The added weight of multiple actuators limits the navigating performance of soft-bodied robots. In this paper, we propose a simple tendon-driven flexible beam with only one actuator (a DC motor) that can generate a mechanical traveling wave along the beam to support the undulation locomotion of soft robots. The beam will be precompressed along its axis by shortening the length of the two tendons to form an S-shape, thus pretensioning the tendons. The motor will wind and unwind the tendons to deform the flexible beam and generate traveling waves along the body of the robot. We experiment with different pre-tension to characterize the relationship between tendon pre-tension forces and the DC-motor winding/unwinding. Our proposal enables a simple implementation of undulation motion to support the locomotion of soft-bodied robots.

Robotic-CLIP: Fine-tuning CLIP on Action Data for Robotic Applications

Vision language models have played a key role in extracting meaningful features for various robotic applications. Among these, Contrastive Language-Image Pretraining (CLIP) is widely used in robotic tasks that require both vision and natural language understanding. However, CLIP was trained solely on static images paired with text prompts and has not yet been fully adapted for robotic tasks involving dynamic actions. In this paper, we introduce Robotic-CLIP to enhance robotic perception capabilities. We first gather and label large-scale action data, and then build our Robotic-CLIP by fine-tuning CLIP on 309,433 videos (~7.4 million frames) of action data using contrastive learning. By leveraging action data, Robotic-CLIP inherits CLIP's strong image performance while gaining the ability to understand actions in robotic contexts. Intensive experiments show that our Robotic-CLIP outperforms other CLIP-based models across various language-driven robotic tasks. Additionally, we demonstrate the practical effectiveness of Robotic-CLIP in real-world grasping applications.