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.

Architects adopt visual scripting and parametric design tools to explore more expansive design spaces (Coates, 2010), refine their thinking about the geometric logic of their design (Woodbury, 2010), and overcome conventional software limitations (Burry, 2011). Despite two decades of effort to make design scripting more accessible, a disconnect between a designer's free ways of thinking and the rigidity of algorithms remains (Burry, 2011). Recent developments in Large Language Models (LLMs) suggest this might soon change, as LLMs encode a general understanding of human context and exhibit the capacity to produce geometric logic. This project speculates that if LLMs can effectively mediate between user intent and algorithms, they become a powerful tool to make scripting in design more widespread and fun. We explore if such systems can interpret natural language prompts to assemble geometric operations relevant to computational design scripting. In the system, multiple layers of LLM agents are configured with specific context to infer the user intent and construct a sequential logic. Given a user's high-level text prompt, a geometric description is created, distilled into a sequence of logic operations, and mapped to software-specific commands. The completed script is constructed in the user's visual programming interface. The system succeeds in generating complete visual scripts up to a certain complexity but fails beyond this complexity threshold. It shows how LLMs can make design scripting much more aligned with human creativity and thought. Future research should explore conversational interactions, expand to multimodal inputs and outputs, and assess the performance of these tools.

Biological lifeforms can heal, grow, adapt, and reproduce -- abilities essential for sustained survival and development. In contrast, robots today are primarily monolithic machines with limited ability to self-repair, physically develop, or incorporate material from their environments. A key challenge to such physical adaptation has been that while robot minds are rapidly evolving new behaviors through AI, their bodies remain closed systems, unable to systematically integrate new material to grow or heal. We argue that open-ended physical adaptation is only possible when robots are designed using only a small repertoire of simple modules. This allows machines to mechanically adapt by consuming parts from other machines or their surroundings and shedding broken components. We demonstrate this principle using a truss modular robot platform composed of one-dimensional actuated bars. We show how robots in this space can grow bigger, faster, and more capable by consuming materials from their environment and from other robots. We suggest that machine metabolic processes akin to the one demonstrated here will be an essential part of any sustained future robot ecology.

The main concern of this study is to find the optimal design of truss structures considering sizing and layout variables simultaneously. As compared to purely sizing optimization problems, this problem is more challenging since the two types of variables involved are fundamentally different in nature. In this paper, a reinforcement learning method combining the update process and Monte Carlo tree search called the update Monte Carlo tree search (UMCTS) for sizing optimization problems is applied to solve combined sizing and layout optimization for truss structures. This study proposes a novel update process for nodal coordinates with two features. (1) The allowed range of each coordinate varies in each round. (2) Accelerators for the number of entries in the allowed range and iteration numbers are introduced to reduce the computation time. Furthermore, nodal coordinates and member areas are determined at the same time with only one search tree in each round. The validation and efficiency of the UMCTS are tested on benchmark problems of planar and spatial trusses with discrete sizing variables and continuous layout variables. It is shown that the CPU time of the UMCTS is two times faster than the branch and bound method. The numerical results demonstrate that the proposed method stably achieves a better solution than other traditional methods.

Truss robots are highly redundant parallel robotic systems that can be applied in a variety of scenarios. The variable topology truss (VTT) is a class of modular truss robots. As self-reconfigurable modular robots, a VTT is composed of many edge modules that can be rearranged into various structures depending on the task. These robots change their shape by not only controlling joint positions as with fixed morphology robots, but also reconfiguring the connectivity between truss members in order to change their topology. The motion planning problem for VTT robots is difficult due to their varying morphology, high dimensionality, the high likelihood for self-collision, and complex motion constraints. In this paper, a new motion planning framework to dramatically alter the structure of a VTT is presented. It can also be used to solve locomotion tasks that are much more efficient compared with previous work. Several test scenarios are used to show its effectiveness. Supplementary materials are available at https://www.modlabupenn.org/vtt-motion-planning/.

Sizing optimization of truss structures is a complex computational problem, and the reinforcement learning (RL) is suitable for dealing with multimodal problems without gradient computations. In this paper, a new efficient optimization algorithm called update Monte Carlo tree search (UMCTS) is developed to obtain the appropriate design for truss structures. UMCTS is an RL-based method that combines the novel update process and Monte Carlo tree search (MCTS) with the upper confidence bound (UCB). Update process means that in each round, the optimal cross-sectional area of each member is determined by search tree, and its initial state is the final state in the previous round. In the UMCTS algorithm, an accelerator for the number of selections for member area and iteration number is introduced to reduce the computation time. Moreover, for each state, the average reward is replaced by the best reward collected on the simulation process to determine the optimal solution. The proposed optimization method is examined on some benchmark problems of planar and spatial trusses with discrete sizing variables to demonstrate the efficiency and validity. It is shown that the computation time for the proposed approach is at least ten times faster than the branch and bound (BB) method. The numerical results indicate that the proposed method stably achieves better solution than other conventional methods.

In this work, geometry optimization of mechanical truss using computer-aided finite element analysis is presented. The shape of the truss is a dominant factor in determining the capacity of load it can bear. At a given parameter space, our goal is to find the parameters of a hull that maximize the load-bearing capacity and also don't yield to the induced stress. We rely on finite element analysis, which is a computationally costly design analysis tool for design evaluation. For such expensive to-evaluate functions, we chose Bayesian optimization as our optimization framework which has empirically proven sample efficient than other simulation-based optimization methods. By utilizing Bayesian optimization algorithms, the truss design involves iteratively evaluating a set of candidate truss designs and updating a probabilistic model of the design space based on the results. The model is used to predict the performance of each candidate design, and the next candidate design is selected based on the prediction and an acquisition function that balances exploration and exploitation of the design space. Our result can be used as a baseline for future study on AI-based optimization in expensive engineering domains especially in finite element Analysis.

Living creatures and machines interact with the world through their morphology and motions. Recent advances in creating bio-inspired morphing robots and machines have led to the study of variable geometry truss (VGT), structures that can approximate arbitrary geometries and has large degree of freedom to deform. However, they are limited to simple geometries and motions due to the excessively complex control system. While a recent work PneuMesh solves this challenge with a novel VGT design that introduces a selective channel connection strategy, it imposes new challenge in identifying effective channel groupings and control methods. Building on top of the hardware concept presented in PneuMesh, we frame the challenge into a co-design problem and introduce a learning-based model to find a sub-optimal design. Specifically, given an initial truss structure provided by a human designer, we first adopt a genetic algorithm (GA) to optimize the channel grouping, and then couple GA with reinforcement learning (RL) for the control. The model is tailored to the PneuMesh system with customized initialization, mutation and selection functions, as well as the customized translation-invariant state vector for reinforcement learning. The result shows that our method enables a robotic table-based VGT to achieve various motions with a limited number of control inputs. The table is trained to move, lower its body or tilt its tabletop to accommodate multiple use cases such as benefiting kids and painters to use it in different shape states, allowing inclusive and adaptive design through morphing trusses.

An exoskeleton "boot" that allows people to walk 9% faster with 17% less effort has been developed by scientists. This robotic footwear comes with a motor that works with calf muscles to give the wearer an extra push with every step, researchers from Stanford University in the US said. The team said its work, which is published in the journal Nature, could help people with mobility impairments "move throughout the world as they like". Patrick Slade, who worked on the exoskeleton as a PhD student at the Stanford Biomechatronics Laboratory and is the first author on the study, told the PA news agency: "There are a number of clinical populations we hope to help including older adults, people with muscle weakness from a variety of conditions like stroke, and specific injury recoveries for things like achilles tendon strain. "We are starting to perform studies to explore the benefits of using our device with older adults.

Model serving isn't just a hard problem, it's a hard problem that constantly demands new solutions. Model serving, as part of MLOps, is the DevOps challenge of keeping a complicated, fragile artifact (the model) working in multiple dynamic environments. As frameworks are built and updated for training models, and production environments evolve for new capabilities and constraints, data scientists have to reimplement model serving scripts and rebuild model deployment processes. Data scientists working in large, well-resourced organizations can hand off their models to specialized MLOps teams for serving and deployment. But for those of us working at start-ups and newer companies, like I did for the first decade of my career, we had to handle the ML deployment challenge ourselves.