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.

Robots in the real world experience wear and tear, leading to changing system dynamics. This challenge is particularly exacerbated for non-rigid systems such as soft robots or robotic systems made of metamaterials with hysteresis. This setting results in a challenging problem for most learning-based controllers that typically rely on the assumption that the system dynamics remain fixed over time. In the absence of explicit mechanisms to account for this change in dynamics, learning-based control algorithms show considerable degradation in performance over time. In this work, we consider a particular class of dynamics shift in under-actuated systems, that is localized to the dynamics of the fully actuated robot itself, while independently leaving the dynamics of the environment unchanged. This captures real-world phenomena such as fatigue or hysteresis in robotic systems. In this setting, we propose an efficient algorithm that can account for dynamics shift. Using a simple calibration procedure, we propose a technique for learning a non-linear “action-translation” model that can capture the localized shift in dynamics. This enables continual learning and transfer despite considerable dynamics shift during the learning process. We demonstrate the efficacy of this procedure on several tasks in simulation, as well as a real-world robotic system - a 4 DoF electrically driven handed shearing auxetic (HSA) platform.

In many robotic systems, the holding state consumes power, limits operating time, and increases operating costs. Electrostatic clutches have the potential to improve robotic performance by generating holding torques with low power consumption. A key limitation of electrostatic clutches has been their low specific shear stresses which restrict generated holding torque, limiting many applications. Here we show how combining the Johnsen-Rahbek (JR) effect with the exponential tension scaling capstan effect can produce clutches with the highest specific shear stress in the literature. Our system generated 31.3 N/cm2sheer stress and a total holding torque of 7.1 N•m while consuming only 2.5 mW/cm2at 500 V. We demonstrate a theoretical model of an electrostatic adhesive capstan clutch and demonstrate how large angle (θ > 2π) designs increase efficiency over planar or small angle (θ < π) clutch designs. We also report the first unfilled polymeric material, polybenzimidazole (PBI), to exhibit the JR-effect.

Handed Shearing Auxetics (HSA) are a promising structure for making electrically driven robots with distributed compliance that convert a motors rotation and torque into extension and force. These structures expand and contract by changing an internal angle between links, the evolution of the structure as this angle changes is known as the auxetic trajectory. We overcome past limitations on the range of actuation, blocked force, and stiffness by focusing on two key design parameters: the point of an HSA's auxetic trajectory that is energetically preferred, and the number of cells along the HSAs length. Modeling the HSA as a programmable spring, we characterize the effect of both on blocked force, minimum energy length, spring constant, angle range and holding torque. We also examined the effect viscoelasticity has on actuation forces over time. By varying the preferred auxetic trajectory point, we were able to make actuators that can push, pull, or do both. We expanded the range of forces possible from 5 N to 150 N, and the range of stiffness from 2 N/mm to 89 N/mm. For a fixed point on the auxetic trajectory, we found decreasing length can improve force output, at the expense of needing higher torques, and having a shorter throw. We also found that the viscoelastic effects can limit the amount of force a 3D printed HSA can apply over time.

We present Kinergy—an interactive design tool for creating self-propelled motion by harnessing the energy stored in 3D printable springs. To produce controllable output motions, we introduce 3D printable kinetic units, a set of parameterizable designs that encapsulate 3D printable springs, compliant locks, and transmission mechanisms for three non-periodic motions—instant translation, instant rotation, continuous translation—and four periodic motions—continuous rotation, reciprocation, oscillation, intermittent rotation. Kinergy allows the user to create motion-enabled 3D models by embedding kinetic units, customize output motion characteristics by parameterizing embedded springs and kinematic elements, control energy by operating the specialized lock, and preview the resulting motion in an interactive environment. We demonstrate the potential of our techniques via example applications from spring-loaded cars to kinetic sculptures and close with a discussion of key challenges such as geometric constraints.

This work proposes a novel generative design tool for passive grippers---robot end effectors that have no additional actuation and instead leverage the existing degrees of freedom in a robotic arm to perform grasping tasks. Passive grippers are used because they offer interesting trade-offs between cost and capabilities. However, existing designs are limited in the types of shapes that can be grasped. This work proposes to use rapid-manufacturing and design optimization to expand the space of shapes that can be passively grasped. Our novel generative design algorithm takes in an object and its positioning with respect to a robotic arm and generates a 3D printable passive gripper that can stably pick the object up. To achieve this, we address the key challenge of jointly optimizing the shape and the insert trajectory to ensure a passively stable grasp. We evaluate our method on a testing suite of 22 objects (23 experiments), all of which were evaluated with physical experiments to bridge the virtual-to-real gap. Code and data are at https://homes.cs.washington.edu/~milink/passive-gripper/

Bladed tools such as jigsaws are common tools for wood workers on job-sites and in workshops, but do not currently have sufficient autonomous hardware or path planning algorithms to enable automation. Here we present a system of an autonomous robot and a path planning algorithm for automating jigsaw operations. The robot can drill holes, insert the jigsaw, and cut plywood. Our algorithm converts complex shapes into paths for the jigsaw, drill holes, and traversal movements for the robot. The algorithm decomposes input shapes into cuttable sections and determines possible locations for drilling entry holes for inserting the blade. We cast the drill hole problem as a set coverage problem with a trade-off between number of holes and cutting distance. We characterize the algorithm on a series of shapes and determined the algorithm found valid solutions. We executed an example on the robot to demonstrate the end-to-end system.

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.

As artificial intelligence becomes an increasingly prevalent method of enhancing robotic capabilities, it is important to consider effective ways to train these learning pipelines and to leverage human expertise. Working towards these goals, a master-apprentice model is presented and is evaluated during a grasping task for effectiveness and human perception. The apprenticeship model augments self-supervised learning with learning by demonstration, efficiently using the human's time and expertise while facilitating future scalability to supervision of multiple robots; the human provides demonstrations via virtual reality when the robot cannot complete the task autonomously. Experimental results indicate that the robot learns a grasping task with the apprenticeship model faster than with a solely self-supervised approach and with fewer human interventions than a solely demonstration-based approach; 100% grasping success is obtained after 150 grasps with 19 demonstrations. Preliminary user studies evaluating workload, usability, and effectiveness of the system yield promising results for system scalability and deployability. They also suggest a tendency for users to overestimate the robot's skill and to generalize its capabilities, especially as learning improves.

Spatial mapping of surface roughness is a critical enabling technology for automating adaptive sanding operations. We leverage GelSight sensors to convert the problem of surface roughness measurement into a vision classification problem. By combining GelSight sensors with Optitrack positioning systems we attempt to develop an accurate spatial mapping of surface roughness that can compare to human touch, the current state of the art for large scale manufacturing. To perform the classification, we propose the use of Bayesian neural networks in conjunction with uncertainty-aware prediction. We compare the sensor and network with a human baseline for both absolute and relative texture classification. To establish a baseline, we collected performance data from humans on their ability to classify materials into 60, 120, and 180 grit sanded pine boards. Our results showed that the probabilistic network performs at the level of human touch for absolute and relative classifications. Using the Bayesian approach enables establishing a confidence bound on our prediction. We were able to integrate the sensor with Optitrack to provide a spatial map of sanding grit applied to pine boards. From this result, we can conclude that GelSight with Bayesian neural networks can learn accurate representations for sanding, and could be a significant enabling technology for closed loop robotic sanding operations.

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.

Traditional manufacturing workflows strongly decouple design and fabrication phases. As a result, fabrication-related objectives such as manufacturing time and precision are difficult to optimize in the design space, and vice versa. This paper presents HL-HELM, a high-level, domain-specific language for expressing abstract, parametric fabrication plans; it also introduces LL-HELM, a low-level language for expressing concrete fabrication plans that take into account the physical constraints of available manufacturing processes. We present a new compiler that supports the real-time, unoptimized translation of high-level, geometric fabrication operations into concrete, tool-specific fabrication instructions; this gives users immediate feedback on the physical feasibility of plans as they design them. HELM offers novel optimizations to improve accuracy and reduce fabrication time as well as material costs. Finally, optimized low-level plans can be interpreted as step-by-step instructions for users to actually fabricate a physical product. We provide a variety of example fabrication plans in the carpentry domain that are designed using our high-level language, show how the compiler translates and optimizes these plans to generate concrete low-level instructions, and present the final physical products fabricated in wood.

Despite the ubiquity of carpentered items, the customization of carpentered items remains labor intensive. The generation of laymen editable templates for carpentry is difficult. Current design tools rely heavily on CNC fabrication, limiting applicability. We develop a template based system for carpentry and a robotic fabrication system using mobile robots and standard carpentry tools. Our end-to-end design and fabrication tool democratizes design and fabrication of carpentered items. Our method combines expert knowledge for template design, allows laymen users to customize and verify specific designs, and uses robotics system to fabricate parts. We validate our system using multiple designs to make customizable, verifiable templates and fabrication plans and show an end-to-end example that was designed, manufactured, and assembled using our tools.

We present a distributed control strategy for the aggregation of multiple modular robots into one connected structure optimized for use with 3D modular pivoting cube robots such as the 3D M-Blocks [1]. We use the intensity from a light source as input to a decentralized control algorithm that drives the robots together. We describe the algorithm, give provable guarantees on convergence, and discuss experiments carried out in simulation and with a hardware platform of ten 3D M-Blocks modules. In this paper we contribute provably correct algorithms for the aggregation of generic modular robots; we show how these algorithms can be applied on real hardware by evaluating them on the 3D M-Blocks platform.

Linear bladed cutting tools, such as jigsaws and reciprocating saws are vital manufacturing tools for humans. They enable people to cut structures that are much larger than themselves. Robots currently lack a generic path planner for linear bladed cutting tools. We developed a model for bladed tools based on Reeds-Shepp cars, and used the model to make a generic path planning algorithm for closed curves. We built an autonomous mobile robot which can implement the algorithm to cut arbitrarily large shapes in a 2D plane. We tested the robots performance and demonstrated the algorithm on several test cases.

Impact protection and vibration isolation are an important component of the mobile robot designer's toolkit; however, current damping materials are available only in bulk or molded form, requiring manual fabrication steps and restricting material property control. In this paper we demonstrate a new method for 3D printing viscoelastic materials with specified material properties. This method allows arbitrary net-shape material geometries to be rapidly fabricated and enables continuously varying material properties throughout the finished part. This new ability allows robot designers to tailor the properties of viscoelastic damping materials in order to reduce impact forces and isolate vibrations. We present a case study for using this material to create jumping robots with programmed levels of bouncing.