Robotics presents a promising opportunity for enhancing bathing assistance, potentially to alleviate labor shortages and reduce care costs, while offering consistent and gentle care for individuals with physical disabilities. However, ensuring flexible and efficient cleaning of the human body poses challenges as it involves direct physical contact between the human and the robot, and necessitates simple, safe, and effective control. In this paper, we introduce a soft, expandable robotic manipulator with embedded capacitive proximity sensing arrays, designed for safe and efficient bed bathing assistance. We conduct a thorough evaluation of our soft manipulator, comparing it with a baseline rigid end effector in a human study involving 12 participants across 96 bathing trails. Our soft manipulator achieves an an average cleaning effectiveness of 88.8% on arms and 81.4% on legs, far exceeding the performance of the baseline. Participant feedback further validates the manipulator’s ability to maintain safety, comfort, and thorough cleaning. https://sites.google.com/view/softbathing.

In biological swarms, army ants and bees have demonstrated the ability to form functional structures for collaborative tasks. Achieving similar functionality with robot swarms requires forming connections between robots using electrical, magnetic, or mechanical means. Our research introduces the PuzzleBots–robot swarms equipped with passive coupling mechanisms that enable collective behavior. These mechanisms leverage the individual mobility and dexterity of each robot to achieve complex assemblies. By coupling together, PuzzleBots can form both rigid and flexible structures that significantly enhance their ability to navigate challenging terrains. Rigid structures offer high load-bearing and transportation capabilities, while flexible structures provide compliance with environmental geometries. We demonstrated that these assembled structures can be precisely controlled using our distributed Model Predictive Control framework. Our results show that passive coupling in robot swarms significantly improves the traversal capability on rough and discontinuous terrains compared with individual robots.

Springtails are tiny arthropods that crawl and jump. They jump by temporarily storing elastic energy in resilin elastic cuticular structures and releasing that energy to accelerate a tail, called a furca, propelling them in the air. This paper presents an autonomous, springtail-inspired microrobot that can crawl and jump. The microrobot has a mass of 980 mg and stands 13 mm tall, and has on-board sensing, computation, and power, enabling autonomy. The microrobot was designed with a super-elastic shape memory alloy (SMA) spring that is manually loaded to store elastic energy. The on-board sensing and computation triggers an actuator at the jump frequency range that unlatches the spring, launching the microrobot into the air at speeds up to 3.171 m/s. At the same time, the microrobot is capable of crawling, when actuated at frequencies lower or higher than the jump frequency range, demonstrating autonomous multi-modal locomotion. This work opens up new pathways toward autonomy in multi-modal microrobots.

Urban search and rescue (USAR) remains a challenging application domain despite recent and rapid advancements in robotics. Navigating complex environments is difficult for robotic architectures due to the variation in surface composition and the range of terrain feature sizes. This variety of scenarios incentivize creating a range of different robot designs, but how to effectively use these robots together remains uncertain. Soft robots and centimeter (cm) scale robots have been studied for their potential uses in USAR operations due to their ability to pass through narrow spaces by deforming to adapt to the environment or by virtue of their small size. However, both classes still poses significant challenges in control, limited operational domain, and runtime. In this paper, we demonstrate how collaborative behavior of Vine Robots, i.e. soft inflatable growing robots, and RESCUE Rollers, i.e. cm-scale mobile robots, can expand the reachable area for each robot in rescue-like operations. We accomplish this expansion by equipping the Vine robot to carry and deploy the RESCUE Rollers, and by using the RESCUE Rollers to improve the maneuvarability of Vine Robots by external steering. Through experimental characterization of the necessary interaction forces and an example scenario of collaborative behavior to reach the top of a ramp, we demonstrate that this approach of physical heterogeneous collaboration can lead to increased capabilities of all systems involved.

Dexterous robotic manipulation remains a challenging domain due to its strict demands for precision and robustness on both hardware and software. While dexterous robotic hands have demonstrated remarkable capabilities in complex tasks, efficiently learning adaptive control policies for hands still presents a significant hurdle given the high dimensionalities of hands and tasks. To bridge this gap, we propose Tilde, an imitation learning-based in-hand manipulation system on a dexterous DeltaHand. It leverages 1) a low-cost, configurable, simple-to-control, soft dexterous robotic hand, DeltaHand, 2) a user-friendly, precise, real-time teleoperation interface, TeleHand, and 3) an efficient and generalizable imitation learning approach with diffusion policies. Our proposed TeleHand has a kinematic twin design to the DeltaHand that enables precise one-to-one joint control of the DeltaHand during teleoperation. This facilitates efficient high-quality data collection of human demonstrations in the real world. To evaluate the effectiveness of our system, we demonstrate the fully autonomous closed-loop deployment of diffusion policies learned from demonstrations across seven dexterous manipulation tasks with an average 90% success rate.

Dexterous robotic manipulation in unstructured environments can aid in everyday tasks such as cleaning and caretaking. Anthropomorphic robotic hands are highly dexterous and theoretically well-suited for working in human domains, but their complex designs and dynamics often make them difficult to control. By contrast, parallel-jaw grippers are easy to control and are used extensively in industrial applications, but they lack the dexterity for various kinds of grasps and in-hand manipulations. In this work, we present DeltaHands , a synergistic dexterous hand framework with Delta robots. The DeltaHands are soft, easy to reconfigure, simple to manufacture with low-cost off-the-shelf materials, and possess high degrees of freedom that can be easily controlled. By leveraging hand synergies, DeltaHands ’ dexterity can be adjusted for different applications with further reduced control complexity. We characterize the Delta robots' kinematics accuracy, force profiles, and workspace range to assist with hand design. Finally, we evaluate the versatility of DeltaHands by grasping a diverse set of objects and by using teleoperation to complete three dexterous manipulation tasks: cloth folding, cap opening, and cable arrangement.

Planning robot dexterity is challenging due to the non-smoothness introduced by contacts, intricate fine motions, and ever-changing scenarios. We present a hierarchical planning framework for dexterous robotic manipulation (HiDex). This framework explores in-hand and extrinsic dexterity by leveraging contacts. It generates rigid-body motions and complex contact sequences. Our framework is based on Monte-Carlo Tree Search (MCTS) and has three levels: 1) planning object motions and environment contact modes; 2) planning robot contacts; 3) path evaluation and control optimization. This framework offers two main advantages. First, it allows efficient global reasoning over high-dimensional complex space created by contacts. It solves a diverse set of manipulation tasks that require dexterity, both intrinsic (using the fingers) and extrinsic (also using the environment), mostly in seconds. Second, our framework allows the incorporation of expert knowledge and customizable setups in task mechanics and models. It requires minor modifications to accommodate different scenarios and robots. Hence, it provides a flexible and generalizable solution for various manipulation tasks. As examples, we analyze the results on 7 hand configurations and 15 scenarios. We demonstrate 8 tasks on two robot platforms.

Reconfigurable robot swarms are capable of connecting with each other to form complex structures. Current mechanical or magnetic connection mechanisms can be complicated to manufacture, consume high power, have a limited load-bearing capacity, or can only form rigid structures. In this paper, we present our low-cost soft anchor design that enables flexible coupling and decoupling between robots. Our asymmetric anchor requires minimal force to be pushed into the opening of another robot while having a strong pulling force so that the connection between robots can be secured. To maintain this flexible coupling mechanism as an assembled structure, we present our Model Predictive Control (MPC) frameworks with polygon constraints to model the geometric relationship between robots. We conducted experiments on the soft anchor to obtain its force profile, which informed the three-bar linkage model of the anchor in the simulations. We show that the proposed mechanism and MPC frameworks enable the robots to couple, decouple, and perform various behaviors in both the simulation environment and hardware platform. Our code is available at https://github.com/ZoomLabCMU/puzzlebot_anchor . Video is available at https://www.youtube.com/watch?v=R3gFplorCJg .

The potential of ingestible medical devices can be greatly enhanced through the use of smart structures made from stimuli-responsive materials. While hydration is a convenient stimulus for inducing shape changes in biomaterials, finding robust materials that can achieve rapid actuation, facile manufacturability, and biocompatibility suitable for ingestible medical devices poses practical challenges. Hydration is a convenient stimulus to induce shape changes in smart biomaterials; however, there are many practical challenges to identifying materials that can achieve rapid actuation and facile manufacturability while satisfying constraints associated with biocompatibility requirements and mechanical properties that are suitable for ingestible medical devices. Herein, we illustrate the formulation and processability of a moisture-responsive genipin-crosslinked gelatin bioplastic system, which can be processed into complex three-dimensional shapes. Mechanical characterization of bioplastic samples showed Young's Modulus values as high as 1845 MPa and toughness values up to 52 MJ/m3, using only food-safe ingredients. Custom molds and UV-laser processing enabled the fabrication of centimeter-scale structures with over 150 independent actuating joints. These self-actuating structures soften and unfold in response to surrounding moisture, eliminating the need for additional stimuli or actuating elements.

This paper presents a new type of distributed dexterous manipulator: delta arrays. Our delta array setup consists of 64 linearly-actuated delta robots with 3D-printed compliant linkages. Through the design of the individual delta robots, the modular array structure, and distributed communication and control, we study a wide range of in-plane and out-of-plane manipulations, as well as prehensile manipulations among subsets of neighboring delta robots. We also demonstrate dexterous manipulation capabilities of the delta array using reinforcement learning while leveraging compliance. Our evaluations show that the resulting 192 DoF compliant robot is capable of performing various coordinated distributed manipulations of a variety of objects, including translation, alignment, prehensile squeezing, lifting, and grasping.

The designs of origami-and kirigami-inspired robots enable configurations from 2D to 3D shapes, lightweight systems, and take advantage of rapid fabrication techniques. These features have been explored for robotics in applications ranging from aerospace to medical devices. However, achieving reversible reconfigurations that sustain/lock between shapes without requiring constant energy input and allow system integration (e.g., sensing, assembly) is challenging. This letter proposes a design and fabrication approach that uses electrically driven mechanisms to enable reversible self-reconfiguration and locking without constant energy input. We leverage origami and kirigami-inspired designs to transmit the motions of a planar artificial muscle and low melting point alloys for time-controlled locking. Using these techniques, we demonstrate compact systems in multiple reconfigurable robotic applications, from gripping to crawling.

This paper presents the DeltaZ robot, a centimeter-scale, low-cost, delta-style robot that allows for a broad range of capabilities and robust functionalities. Current technologies allow DeltaZ to be 3D-printed from soft and rigid materials so that it is easy to assemble and maintain, and lowers the barriers to utilize. Functionality of the robot stems from its three translational degrees of freedom and a closed form kinematic solution which makes manipulation problems more intuitive compared to other manipulators. Moreover, the low cost of the robot presents an opportunity to democratize manipulators for a research setting. We also describe how the robot can be used as a reinforcement learning benchmark. Open-source 3D-printable designs and code are available to the public.

In this paper, we present a heterogeneous robot swarm system that can physically couple with each other to form functional structures and dynamically decouple to perform individual tasks. The connection between robots can be formed with a passive coupling mechanism, ensuring minimum energy consumption during coupling and decoupling behavior. The heterogeneity of the system enables the robots to perform structural enhancement configurations based on specific environmental requirements. We propose a connection-pair oriented configuration control algorithm to form different assemblies. We show experiments of up to nine robots performing the coupling, gap-crossing, and decoupling behaviors.

Robotic manipulation of cloth has applications ranging from fabrics manufacturing to handling blankets and laundry. Cloth manipulation is challenging for robots largely due to their high degrees of freedom, complex dynamics, and severe self-occlusions when in folded or crumpled configurations. Prior work on robotic manipulation of cloth relies primarily on vision sensors alone, which may pose challenges for fine-grained manipulation tasks such as grasping a desired number of cloth layers from a stack of cloth. In this paper, we propose to use tactile sensing for cloth manipulation; we attach a tactile sensor (ReSkin) to one of the two fingertips of a Franka robot and train a classifier to determine whether the robot is grasping a specific number of cloth layers. During test-time experiments, the robot uses this classifier as part of its policy to grasp one or two cloth layers using tactile feedback to determine suitable grasping points. Experimental results over 180 physical trials suggest that the proposed method outperforms baselines that do not use tactile feedback and has better generalization to unseen cloth compared to methods that use image classifiers. Code, data, and videos are available at https://sites.google.com/view/reskin-cloth.

Robotic bathing assistance has long been considered an important and practical task in healthcare. Yet, achieving flexible and efficient cleaning tasks on the human body is challenging, since washing the body involves direct human-robot physical contact and simple, safe, and effective devices are needed for bathing and hygiene. In this paper, we present a meso-scale wearable robot that can locomote along the human body to provide bathing and skin care assistance. We evaluated the cleaning performance of the robot system under different scenarios. The experiments on the pipe show that the robot can achieve cleaning percentage over 92% with two types of stretchable fabrics. The robot removed most of the debris with average values of 94% on a human arm and 93% on a manikin torso. The results demonstrate that the robot exhibits high performance in cleaning tasks.

Traditional parallel-jaw grippers are insufficient for delicate object manipulation due to their stiffness and lack of dexterity. Other dexterous robotic hands often have bulky fingers, rely on complex time-varying cable drives, or are prohibitively expensive. In this paper, we introduce a novel low-cost compliant gripper with two centimeter-scaled 3-DOF delta robots using off-the-shelf linear actuators and 3D-printed soft materials. To model the kinematics of delta robots with soft compliant links, which diverge from typical rigid links, we train neural networks using a perception system. Furthermore, we analyze the delta robot’s force profile by varying the starting position in its workspace and measuring the resulting force from a push action. Finally, we demonstrate the compliance and dexterity of our gripper through six dexterous manipulation tasks involving small and delicate objects. Thus, we present the groundwork for creating modular multi-fingered hands that can execute precise and low-inertia manipulations.

Distributed manipulators - consisting of a set of actuators or robots working cooperatively to achieve a manipulation task - are robust and flexible tools for performing a range of planar manipulation skills. One novel example is the delta array, a distributed manipulator composed of a grid of delta robots, capable of performing dexterous manipulation tasks using strategies incorporating both dynamic and static contact. Hand-designing effective distributed control policies for such a manipulator can be complex and time consuming, given the high-dimensional action space and unfamiliar system dynamics. In this paper, we examine the principles guiding development and control of such a delta array for a planar translation task. We explore policy learning as a robust cooperative control approach, allowing for smooth manipulation of a range of objects, showing improved accuracy and efficiency over baseline human-designed policies.

We design a compliant delta manipulator using 3D-printing and soft materials. Our design is different from the traditionally rigid delta robots as it is more accessible through low-cost 3D-printing, and can interact safely with its surroundings due to compliance. This work focuses on parallelogram links which are a key component of the delta robot design. We characterize these links over twelve dimensional parameters, such as beam and hinge thickness, and two material stiffness settings by displacing them, and observing the resulting forces and rotation angles. The parallelogram links are then integrated into a delta robot structure to test for delta mechanism behavior, which keeps the end-effector parallel to the base of the robot. We observed that using compliant hinges resulted in near-delta behavior, laying the groundwork for fabricating and utilizing 3D-printed compliant delta manipulators.

In robotics, the origami-based design methodology uses straightforward fabrication and assembly processes to create small-scale parallel mechanisms. Delta mechanisms are among the well-known parallel mechanisms used mostly in pick-and-place operations due to their capability to reach high speeds and accelerations. In this work, we present a novel Delta mechanism based on origami-inspired designs and two-dimensional layer-by-layer fabrication methods, reducing the time and errors in manufacturing. We developed a new flat parallelogram providing translations in X–Y–Z directions, respecting the Delta mechanism’s conventional kinematic models. The fabrication and assembly processes include laser machining and lamination, eliminating manual-folding and bonding steps. The mechanism operates in a 20 × 20 × 20 mm3 workspace and a 17.5 cm diameter circular footprint when it is entirely flat. The kinematic performance of the mechanism is analyzed using a six degrees-of-freedom position sensor on the end effector. The experiments are conducted to follow circular trajectories with varying radii at different heights. Despite having no feedback control from the end effector, the mechanism follows the given trajectories with 1.5 mm root-mean-square (RMS) precision. We also present the effects of the elasticity of flexible materials at different regions of the mechanism on the performance of the Delta robot.

Efficient and effective generation of high-acceleration movement in biology requires a process to control energy flow and amplify mechanical power from power density-limited muscle. Until recently, this ability was exclusive to ultrafast, small organisms, and this process was largely ascribed to the high mechanical power density of small elastic recoil mechanisms. In several ultrafast organisms, linkages suddenly initiate rotation when they overcenter and reverse torque; this process mediates the release of stored elastic energy and enhances the mechanical power output of extremely fast, spring-actuated systems. Here we report the discovery of linkage dynamics and geometric latching that reveals how organisms and synthetic systems generate extremely high-acceleration, short-duration movements. Through synergistic analyses of mantis shrimp strikes, a synthetic mantis shrimp robot, and a dynamic mathematical model, we discover that linkages can exhibit distinct dynamic phases that control energy transfer from stored elastic energy to ultrafast movement. These design principles are embodied in a 1.5-g mantis shrimp scale mechanism capable of striking velocities over 26 m [Formula: see text] in air and 5 m [Formula: see text] in water. The physical, mathematical, and biological datasets establish latching mechanics with four temporal phases and identify a nondimensional performance metric to analyze potential energy transfer. These temporal phases enable control of an extreme cascade of mechanical power amplification. Linkage dynamics and temporal phase characteristics are easily adjusted through linkage design in robotic and mathematical systems and provide a framework to understand the function of linkages and latches in biological systems.