Mark Yim

Modular reconfigurable robot (MRR) systems have attracted growing attention because of their versatility and ability to operate across diverse domains. Research on MRR systems has made notable progress, evolving from laboratory settings to real-world applications. However, a gap remains in aligning MRR technologies with specific needs, and a comprehensive review of challenges in practical applications is still lacking. To address these issues, this Review adopts an application-oriented perspective. Focusing on locomotion, manipulation, and construction, it proposes a previously unreported perspective centered on real-world demands. The Review outlines these needs and identifies major challenges facing MRR systems, categorized into three areas: general hardware challenges, general software challenges, and application-specific challenges. This Review aims to inform the development of MRR systems from theory to practice through a demand-technology-application approach. By working backward from practical applications to technical requirements, it identifies technological gaps in specific use cases. It also surveys current research, highlighting strengths, limitations, and potential directions while emphasizing key challenges for future progress.

Although miniaturization has been a goal in robotics for nearly 40 years, roboticists have struggled to access submillimeter dimensions without making sacrifices to onboard information processing because of the unique physics of the microscale. Consequently, microrobots often lack the key features that distinguish their macroscopic cousins from other machines, namely, on-robot systems for decision-making, sensing, feedback, and programmable computation. Here, we take up the challenge of building a robot comparable in size to a single-celled paramecium that can sense, think, and act using onboard systems for computation, sensing, memory, locomotion, and communication. Built massively in parallel with fully lithographic processing, these microrobots can execute digitally defined algorithms and autonomously change behavior in response to their surroundings. Combined, these results pave the way for general-purpose microrobots that can be programmed many times in a simple setup and can work together to carry out tasks without supervision in uncertain environments.

We propose a new formulation for the multi-robot task allocation problem that incorporates (a) complex precedence relationships between tasks, (b) efficient intra-task coordination, and (c) cooperation through the formation of robot coalitions. A task graph specifies the tasks and their relationships, and a set of reward functions models the effects of coalition size and preceding task performance. Maximizing task rewards is NP-hard; hence, we propose network flow-based algorithms to approximate solutions efficiently. A novel online algorithm performs iterative re-allocation, providing robustness to task failures and model inaccuracies to achieve higher performance than offline approaches. We comprehensively evaluate the algorithms in a testbed with random missions and reward functions and compare them to a mixed- integer solver and a greedy heuristic. Additionally, we validate the overall approach in an advanced simulator, modeling reward functions based on realistic physical phenomena and executing the tasks with realistic robot dynamics. Results establish efficacy in modeling complex missions and efficiency in generating high-fidelity task plans while leveraging task relationships.

Soft pneumatic actuators (SPA) made from elastomeric materials can provide large strain and large force. The behavior of locally strain-restricted hyperelastic materials under inflation has been investigated thoroughly for shape reconfiguration, but requires further investigation for trajectories involving external force. In this work we model force-pressure-height relationships for a concentrically strain-limited class of soft pneumatic actuators and demonstrate the use of this model to design SPA response for object lifting. We predict relationships under different loadings by solving energy minimization equations and verify this theory by using an automated test rig to collect rich data for n = 22 Ecoflex 00-30 membranes. We collect data using an active learning pipeline to efficiently model the design space. We show that this learned model outperforms the theory-based model and a naive regression. We use our model to optimize membrane design for different lift tasks and compare this performance to other designs. These contributions represent a step towards understanding the natural response for this class of actuator and embodying intelligent lifts in a single-pressure input actuator system.

Modular robotics research has long been preoccupied with perfecting the modules themselves -- their actuation methods, connectors, controls, communication, and fabrication. This inward focus results, in part, from the complexity of the task and largely confines modular robots to sterile laboratory settings. The latest generation of truss modular robots, such as the Variable Topology Truss and the Truss Link, have begun to focus outward and reveal a key insight: the environment is not just a backdrop; it is a tool. In this work, we shift the paradigm from building better robots to building better robot environment interactions for modular truss robots. We study how modular robots can effectively exploit their surroundings to achieve faster locomotion, adaptive self-reconfiguration, and complex three-dimensional assembly from simple two-dimensional robot assemblies. By using environment features -- ledges, gaps, and slopes -- we show how the environment can extend the robots' capabilities. Nature has long mastered this principle: organisms not only adapt, but exploit their environments to their advantage. Robots must learn to do the same. This study is a step towards modular robotic systems that transcend their limitations by exploiting environmental features.

Nature manufactures structures via decentralized processes involving groups of agents. This is fundamentally different from traditional manufacturing, where objects are produced via sequences of predefined steps. In this work, we explore the idea of using simulated “swarms” of simple agents to generate new designs for architected materials in a decentralized, bioinspired manner. Individual agents choose their own actions based solely on information in their immediate environment, with no centralized control. The structures that these processes produce are the result of the collective action of the individual agents, rather than a predetermined design. We build an integrated platform for determining “rule-structure-property” relationships, analogous to process-structure-property relationships in materials science. The platform simulates agent behaviors to show how different rules and different environments result in different structures. We then three-dimensional print these and perform finite element analysis to experimentally and numerically characterize mechanical properties, including tensile strength and energy dissipation.

Despite their growing popularity, swarms of robots remain limited by the operating time of each individual. We present algorithms which allow a human to sculpt a swarm of robots into a shape that persists in space perpetually, independent of onboard energy constraints such as batteries. Robots generate a path through a shape such that robots cycle in and out of the shape. Robots inside the shape react to human initiated changes and adapt the path through the shape accordingly. Robots outside the shape recharge and return to the shape so that the shape can persist indefinitely. The presented algorithms communicate shape changes throughout the swarm using message passing and robot motion. These algorithms enable the swarm to persist through any arbitrary changes to the shape. We describe these algorithms in detail and present their performance in simulation and on a swarm of mobile robots. The result is a swarm behavior more suitable for extended duration, dynamic shape-based tasks in applications such as entertainment, agriculture, and emergency response.