Search arXiv⌕ Search

arXiv · 2008.12864

Vacuum Driven Auxetic Switching Structure and Its Application on a Gripper and Quadruped

Abstract

The properties and applications of auxetics have been widely explored in the past years. Through proper utilization of auxetic structures, designs with unprecedented mechanical and structural behaviors can be produced. Taking advantage of this, we present the development of novel and lowcost 3D structures inspired by a simple auxetic unit. The core part, which we call the body in this paper, is a 3D realization of 2D rotating squares. This body structure was formed by joining four similar structures through softer material at the vertices. A monolithic structure of this kind is accomplished through a custom-built multi-material 3D printer. The model works in a way that, when torque is applied along the face of the rotational squares, they tend to bend at the vertex of the softer material, and due to the connected-ness of the design, a proper opening and closing motion is achieved. To demonstrate the potential of this part as an important component for robots, two applications are presented: a soft gripper and a crawling robot. Vacuum-driven actuators move both the applications. The proposed gripper combines the benefits of two types of grippers whose fingers are placed parallel and equally spaced to each other, in a single design. This gripper is adaptable to the size of the object and can grasp objects with large and small cross-sections alike. A novel bending actuator, which is made of soft material and bends in curvature when vacuumed, provides the grasping nature of the gripper. Crawling robots, in addition to their versatile nature, provide a better interaction with humans. The designed crawling robot employs negative pressure-driven actuators to highlight linear and turning locomotion.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shuai Liu, Sheeraz Athar, Michael Yu Wang. 2020-08-28. Vacuum Driven Auxetic Switching Structure and Its Application on a Gripper and Quadruped. https://doi.org/10.1109/iros45743.2020.9341338

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

A Scalable Multi-Robot Framework for Decentralized and Asynchronous Perception-Action-Communication Loops

We develop a decentralized Perception-Action-Communication (PAC) system for multi-robot teams that enables them to collaborate in large scale, outdoor environments. Our system natively supports deployments at any scale by leveraging a graph neural network (GNN) to diffuse information hop-by-hop across the fleet's network. This achieves global collaboration from individual robots limited to local sensing and communication. Fully asynchronous, the core modules of PAC: perception, inter-robot communication, message aggregation and action are clocked at different frequencies with information flowing between them through buffers. We implement the PAC system as a series of highly extensible ROS2 nodes to serve as the foundational infrastructure for deployable swarm systems. PAC is validated in the real world with outdoor experiments with up to N=20 quadrotor robots and in simulations based on real-world data with up to N=100. These validations show that our system upholds crucial properties for field-deployable robot collectives: scalability, resiliency and repeatability.

cs.RO↗

DexWrist: A Robotic Wrist for Constrained and Dynamic Manipulation

Development of dexterous manipulation hardware has primarily focused on hands and grippers. However, these end-effectors are often paired with bulky and highly stiff wrists that limit performance in human environments. More recent designs have adopted backdrivable actuation, but are still difficult to model and control due to coupled kinematics or high mechanical inertia from heavy links. We present DexWrist, a compact robotic wrist combining quasi-direct-drive actuation with a decoupled parallel kinematic mechanism to advance manipulation in highly constrained environments and enable dynamic, contact-rich tasks. It delivers 3.75$\pm$0.05 Nm rated torque, 0.33$\pm$0.06 Nm backdrive torque, 10.15$\pm$1.34 Hz torque bandwidth, $\pm 40^\circ$ ROM in both DOFs, and a diagonal velocity-constraint Jacobian (one-to-one motor-to-DOF mapping) in a 0.97 kg package. In practice, these properties increase workspace in clutter and stabilize contact without finely tuned admittance control. We evaluate DexWrist as a drop-in upgrade in simulation and on three robot arms across constrained and contact-rich tasks. In learned policy evaluations on the AgileX PiPER and UR3e, DexWrist achieved 50-76% relative improvements in success rate and reduced autonomous task completion times by 3-5x; on a torque-controlled Franka FR3, where a strong joint-impedance baseline already succeeds, it still completed the task 1.4x faster. Project page and videos: https://martinpeticco.com/dexwrist

cs.RO↗

Imagine2Act: Leveraging Object-Action Motion Consistency from Imagined Goals for Robotic Manipulation

Relational object rearrangement (ROR) tasks (e.g., insert flower to vase) require a robot to manipulate objects with precise semantic and geometric reasoning. Existing approaches either rely on pre-collected demonstrations that struggle to capture complex geometric constraints or generate goal-state observations to capture semantic and geometric knowledge, but fail to explicitly couple object transformation with action prediction, resulting in errors due to generative noise. To address these limitations, we propose Imagine2Act, a 3D imitation-learning framework that incorporates semantic and geometric constraints of objects into policy learning to tackle high-precision manipulation tasks. We first generate imagined goal images conditioned on language instructions and reconstruct corresponding 3D point clouds to provide robust semantic and geometric priors. These imagined goal point clouds serve as additional inputs to the policy model, while an object-action consistency strategy with soft pose supervision explicitly aligns predicted end-effector motion with generated object transformation. This design enables Imagine2Act to reason about semantic and geometric relationships between objects and predict accurate actions across diverse tasks. Experiments in both simulation and the real world demonstrate that Imagine2Act outperforms previous state-of-the-art policies. More visualizations can be found at https://sites.google.com/view/imagine2act.

cs.RO↗