Luyang Zhao

Modular robots possess great potential due to their adaptability and reconfigurability, yet their use in aquatic environments and dynamic multi-tasking scenarios—particularly for complex manipulation—remains largely underexplored. To address the need for versatile and multifunctional systems in such settings, we hypothesize that integrating soft-bending capabilities into modular robots can create a platform capable of navigating complex environments, performing diverse manipulation tasks, and assembling deformable lattices. In this work, we present a variable-stiffness soft modular robot that combines rigid 3D printed components with soft foam, utilizing a cable-actuated mechanism and a propeller. This modular robot can locomote, bend, steer, connect with other modules, and assemble into various larger active structures for different applications. For instance, when configured as a gripper, the robot can collect trash from the water’s surface. When assembled into a raft, it functions as a movable platform for drone landings. In a chain configuration, the robot moves like a snake on land and transitions seamlessly to aquatic locomotion using a propeller. Additionally, these robots can operate collectively like swarm robots, such as transporting boxes collaboratively across surfaces. Our findings highlight that incorporating deformable features into modular robot designs significantly enhances their adaptability and multifunctionality in aquatic environments.

This paper explores the design and fabrication of a robotic airfoil based on a tensegrity morphing structure. We begin by introducing a family of tensegrity morphing airfoil designs that convert a continuous airfoil shape into a discrete configuration. The airfoil structure is divided into two main components: a rigid section (the D-section head) and a flexible section (the tensegrity morphing tail). We compute aerodynamic forces using the panel method and, based on the drag and lift analyses, propose a morphing airfoil design and develop its CAD model. This model integrates all essential electronics, including the battery, PCB board, and motors, within the rigid D-section. The flexible tail is actuated by strings, enabling adaptive morphing. Our approach integrates lightweight tensegrity principles with adaptive design to create efficient morphing airfoil structures. The methodology is also applicable to bio-inspired wings, robotic fingers, grippers, and other soft robotic systems.

Modular robots are currently designed to perform a variety of tasks, primarily focusing on locomotion or manipulation through the reconfiguration of rigid modules. However, the potential to integrate multiple functions, such as making each robot deployable and capable of building lattice structures for self-construction and infrastructure creation, remains largely unexplored. To advance the field, we hypothesize that combining tensegrity principles with modular robotics can create lightweight, deformable units capable of integrating three critical functions within a single design: navigating varied terrains, manipulating arbitrary shape objects, and assembling weight-sustainable, active large infrastructures. Here, we designed untethered modular robots that are deformable, lightweight, deployable, outdoor-scale, capable of bearing loads, and capable of 3D attachment and detachment. With these characteristics, the system can form various 3D structures using different assembly methods, such as walking into position or being transported by rotorcraft. The deformability and lightweight nature of each block enable the assembled structures to dynamically change shape, providing new capabilities such as added compliance during locomotion and manipulation and the ability to interact with the environment in tasks like tent and bridge assemblies. In summary, we suggest that integrating lightweight and deformable properties into modular robot design offers potential improvements in their adaptability and multi-functionality.

Soft robots offer adaptability and safe interaction with complex environments. Rapid prototyping kits that allow soft robots to be assembled easily will allow different geometries to be explored quickly to suit different environments or to mimic the motion of biological organisms. We introduce SoftSnap modules: snaptogether components that enable the rapid assembly of a class of untethered soft robots. Each SoftSnap module includes embedded computation, motor-driven string actuation, and a flexible thermoplastic polyurethane (TPU) printed structure capable of deforming into various shapes based on the string configuration. These modules can be easily connected with other SoftSnap modules or customizable connectors. We demonstrate the versatility of the SoftSnap system through four configurations: a starfish-like robot, a brittle star robot, a snake robot, a 3D gripper, and a ring-shaped robot. These configurations highlight the ease of assembly, adaptability, and functional diversity of the SoftSnap modules. The SoftSnap modular system offers a scalable, snap-together approach to simplifying soft robot prototyping, making it easier for researchers to explore untethered soft robotic systems rapidly.

This paper presents the first steps toward a soft dolphin robot using a bio-inspired approach to mimic dolphin flexibility. The current dolphin robot uses a minimalist approach, with only two actuated cable-driven degrees of freedom actuated by a pair of motors. The actuated tail moves up and down in a swimming motion, but this first proof of concept does not permit controlled turns of the robot. While existing robotic dolphins typically use revolute joints to articulate rigid bodies, our design – which will be made opensource – incorporates a flexible tail with tunable silicone skin and actuation flexibility via a cable-driven system, which mimics muscle dynamics and design flexibility with a tunable skeleton structure. The design is also tunable since the backbone can be easily printed in various geometries. The paper provides insights into how a few such variations affect robot motion and efficiency, measured by speed and cost of transport (COT). This approach demonstrates the potential of achieving dolphin-like motion through enhanced flexibility in bio-inspired robotics.

This paper introduces the first publicly accessible labeled multi-modal perception dataset for autonomous maritime navigation, focusing on in-water obstacles within the aquatic environment to enhance situational awareness for Autonomous Surface Vehicles (ASVs). This dataset, collected over 4 years and consisting of diverse objects encountered under varying environmental conditions, aims to bridge the research gap in autonomous surface vehicles by providing a multimodal, annotated, and ego-centric perception dataset, for object detection and classification. We also show the applicability of the proposed dataset by training deep learning-based open-source perception algorithms that have shown success. We expect that our dataset will contribute to development of the marine autonomy pipelines and marine (field) robotics. This dataset is open source and can be found at https://seepersea.github.io/.

Traditional swarm robots rely on specific communication and planning strategies to coordinate particular tasks. Human swarms exhibit distinctive characteristics due to their capacity for language-based communication and active reasoning. This paper presents an exploratory approach to robotic swarm intelligence that leverages Large Language Models (LLMs) to emulate human-like active problem-solving behaviors. We introduce a decentralized multi-robot system where each robot initially only has its local information and does not know others’ existence. The robots utilize LLMs for reasoning and natural language for inter-robot communication, enabling them to discover peers, share information, and coordinate actions dynamically. In a series of experiments in zero-shot settings, we observed human-like social behaviors, including mutual discovery, identification, information exchange, collaboration, negotiation, and error correction. While the technical approach is straightforward, the main contribution lies in exploring the interactive societies that LLM-driven robots form—a form of “robot anthropology” that examines emergent collaborative structures.

Recent large language models (LLMs) have demonstrated promising capabilities in modeling real-world knowledge and enhancing knowledge-based generation tasks. In this paper, we further explore the potential of using LLMs to aid in the design of soft modular robots, taking into account both user instructions and physical laws, to reduce the reliance on extensive trial-and-error experiments typically needed to achieve robot designs that meet specific structural or task requirements. Specifically, we formulate the robot design process as a sequence generation task and find that LLMs are able to capture key requirements expressed in natural language and reflect them in the construction sequences of robots. To simplify, rather than conducting real-world experiments to assess design quality, we utilize a simulation tool to provide feedback to the generative model, allowing for iterative improvements without requiring extensive human annotations. Furthermore, we introduce five evaluation metrics to assess the quality of robot designs from multiple angles including task completion and adherence to instructions, supporting an automatic evaluation process. Our model performs well in evaluations for designing soft modular robots with uni- and bi-directional locomotion and stairdescending capabilities, highlighting the potential of using natural language and LLMs for robot design. However, we also observe certain limitations that suggest areas for further improvement.

This paper introduces the first publicly accessible labeled multi-modal perception dataset for autonomous maritime navigation, focusing on in-water obstacles within the aquatic environment to enhance situational awareness for Autonomous Surface Vehicles (ASVs). This dataset, collected over 4 years and consisting of diverse objects encountered under varying environmental conditions, aims to bridge the research gap in autonomous surface vehicles by providing a multi-modal, annotated, and ego-centric perception dataset, for object detection and classification. We also show the applicability of the proposed dataset by training deep learning-based open-source perception algorithms that have shown success. We expect that our dataset will contribute to development of the marine autonomy pipelines and marine (field) robotics. This dataset is opensource and can be found at this https URL.

In this paper, we present a soft modular block inspired by tensegrity structures that can form load-bearing structures through self-assembly. The block comprises a stellated compliant skeleton, shape memory alloy muscles, and permanent magnet connectors. We classify five deformation primitives for individual blocks: bend, compress, stretch, stand, and shrink, which can be combined across modules to reason about full-lattice deformation. Hierarchical function is abundant in nature and in human-designed systems. Using multiple self-assembled lattices, we demonstrate the formation and actuation of 3-dimensional shapes, including a load-bearing pop-up tent, a self-assembled wheel, a quadruped, a block-based robotic arm with gripper, and non-prehensile manipulation. To our knowledge, this is the first example of active deformable modules (blocks) that can reconfigure into different load-bearing structures on-demand.

Natural systems integrate the work of many sub-units (cells) toward a large-scale unified goal (morphological and behav- ioral), which can counteract the effects of unexpected experiences, damage, or simply changes in tasks demands. In this letter, we exploit the opportunities presented by soft, modular, and tensegrity robots to introduce soft lattice modules that parallel the sub-units seen in biological systems. The soft lattice modules are comprised of 3D printed plastic “skeletons,” linear contracting shape mem- ory alloy spring actuators, and permanent magnets that enable adhesion between modules. The soft lattice modules are capable of independent locomotion, and can also join with other modules to achieve collective, self-assembled, larger scale tasks such as collective locomotion and moving an object across the surface of the lattice assembly. This work represents a preliminary step toward soft modular systems capable of independent and collective behaviors, and provide a platform for future studies on distributed control.

Discrete graphs are commonly used to approximately represent configuration spaces used in robot motion planning. This paper explores a representation in which the costs of crossing local regions of the configuration space are represented using piecewise linear regression (PLR). We explore a few simple motion planning problems, and show that for these problems, the memory required to store the representation compares favorably to that required for standard discrete vertex-and-edge models, while preserving the quality of paths returned from searches.

This paper presents a data structure that summarizes distances between configurations across a robot configuration space, using a binary space partition whose cells contain parameters used for a locally linear approximation of the distance function. Querying the data structure is extremely fast, particularly when compared to graph search required for querying Probabilistic Roadmaps, and memory requirements are promising. The paper explores the use of the data structure constructed for a single robot to provide a heuristic for challenging multi-robot motion planning problems. Potential applications also include the use of remote computation to analyze the space of robot motions, which then might be transmitted on-demand to robots with fewer computational resources.

Living with memory loss presents many challenges for patients and caregivers alike. Intelligent assistive technology can help address care gaps. Our core objective is to develop an assistive device that can be used at home by individuals experiencing memory impairment. We propose an assistant, based on existing and widely used voice activated consumer technology, as a tool to help patients and caregivers cope with issues common to cognitive impairment. Our long term objective is to develop a cognitive assistant that can do what is told to do, but also reason and be proactive in interacting with its users.