There is significant interest in creating compliant modular robots that can change their volume. Inspired by how biological cells move, these systems can potentially combine the resilience of modular robotics with the increased environmental interactions of soft robotics. However, current versions have limited speed, expansion, and portability. In this paper, we address these concerns through AuxSwarm, a compliant system composed of auxetic-based robotic voxels. These voxels control their volume through a scissor-like bi-layer auxetic design, growing up to 1.57 times their original size in 0.2 seconds. This combination of speed and expansion is unique across modular soft robots, enabling dynamic locomotion capabilities. We characterize the voxels and demonstrate the versatility of this approach through case studies of 2D bending and 3D cube flipping. AuxSwarm provides a first step towards addressable voxel-based smart materials, while simultaneously addressing the robustness and actuation challenges faced by soft robots.

Sensing plays a pivotal role in robotic manipulation, dictating the accuracy and versatility with which objects are handled. Vision-based sensing methods often suffer from fabrication complexity and low durability, while approaches that rely on direct measurements on the gripper often have limited resolution and are difficult to scale. Here, we present a soft robotic gripper made out of two cubic lattices that are sensorized by embedding air channels within the structure. The lattices are 3D printed from a single build material, simplifying the fabrication process. The flexibility of this approach offers significant control over sensor and lattice design, while the pressure-based internal sensing provides measurements with minimal disruption to the grasping surface. With only 12 sensors, 6 per lattice, this gripper can estimate an object’s weight and location and offer new insights into grasp parameters like friction coefficients and grasp force.

The natural flexibility of soft robotic grippers allows for versatile and compliant grasping. However, this same flexibility can restrict the gripper’s strength. Striking a balance between compliance and strength is essential for effective soft grippers. In this work, we present Flexible Robust Observant Gripper (FROG), a soft gripper that is both compliant and strong. We describe the mechanical design of the gripper, characterize the soft flexures used in the design, and analyze the grasp forces generated by the gripper. Utilizing the structure of the gripper, we develop feedforward grasp controllers and a classifier to distinguish between grasp types. Grasping experiments show that FROG can effectively grasp a variety of objects, including very soft or delicate items. Holding force tests show that our gripper can conform to the grasped object and exert large grasp forces.

Machine learning-based approaches for soft robot proprioception have recently gained popularity, in part due to the difficulties in modeling the relationship between sensor signals and robot shape. However, to date, there exists no systematic analysis of the required design choices to set up a machine learning pipeline for soft robot proprioception. Here, we present the first study examining how design choices on different levels of the machine learning pipeline affect the performance of a neural network for predicting the state of a soft robot. We address the most frequent questions researchers face, such as how to choose the appropriate sensor and actuator signals, process input and output data, deal with time series, and pick the best neural network architecture. By testing our hypotheses on data collected from two vastly different systems–an electrically actuated robotic platform and a pneumatically actuated soft trunk–we seek conclusions that may generalize beyond one specific type of soft robot and hope to provide insights for researchers to use machine learning for soft robot proprioception.

The success of hybrid suction + parallel-jaw grippers in the Amazon Robotics/Picking Challenge have demonstrated the effectiveness of multimodal grasping approaches. However, existing multimodal grippers combine grasping modes in isolation and do not incorporate the benefits of compliance found in soft robotic manipulators. In this paper, we present a gripper that integrates three modes of grasping: suction, parallel jaw, and soft fingers. Using complaint handed shearing auxetics actuators as the foundation, this gripper is able to multiplex manipulation by creating unique grasping primitives through permutations of these grasping techniques. This gripper is able to grasp 88% of tested objects, 14% of which could only be grasped using a combination of grasping modes. The gripper is also able to perform in-hand object re-orientation of flat objects without the need for pre-grasp manipulation.

Compliant robotic grippers are more robust to uncertainties in grasping and manipulation tasks, especially when paired with tactile and proprioceptive feedback. Although considerable progress has been made towards achieving proprioceptive soft robotic grippers, current efforts require complex driving hardware or fabrication techniques. In this paper, we present a simple scalable soft robotic gripper integrated with high-deformation strain and pressure sensors. The gripper is composed of structurally-compliant handed shearing auxetic structures actuated by electric motors. Coupling deformable sensors with the compliant grippers enables gripper proprioception and object classification. With this sensorized system, we are able to identify objects' size to within 33% of actual radius and sort objects as hard/soft with 78% accuracy.

Volume change has become a critical actuation method in robotics. However, the need for fluid flow or thermal processes to generate volume changes limits the durability, speed, and efficiency of these actuators. In this paper, we develop a new electromechanical actuator that volumetrically expands. By combining auxetic materials with a servo, we produce a simple isotropically expanding actuator that can be modularly composed. We discuss the symmetry considerations in selecting an appropriate auxetic framework for our actuator, eventually choosing a double-layered polyhedral auxetic design. Characterization shows that a single actuator can expand in radius to 119% of the original size and generate 90N of force, while maintaining a small package and a speedy expansion / contraction cycle. Finally, we demonstrate the modularity of our actuators by linking three actuators to create a vertical tube-crawling robot. The small package and fast cycle time of our system highlight how viable these electromechanical volumetric actuators can be as an important actuator modality.