Robotic Manipulation Surfaces (RMS) manipulate objects by deforming the surface on which they rest, offering safe, parallel handling of diverse and fragile items. However, existing designs face a fundamental tradeoff: achieving fine control typically demands dense actuator arrays that limit scalability. Modular architectures can extend the workspace, but transferring objects reliably across module boundaries on soft, continuously deformable surfaces remains an open challenge. We present a multi-modular soft manipulation platform that achieves coordinated inter-module object transfer and precise positioning across interconnected fabric-based modules. A hierarchical control framework, combining conflict-free Manhattan-based path planning with directional object passing and a geometric PID controller, achieves sub-centimeter positioning and consistent transfer of heterogeneous objects including fragile items. The platform employs shared-boundary actuation, where adjacent modules share edge actuators, reducing the required count from 4n^2 to (n + 1)^2 for an n × n grid; a 2× 2 prototype covers 1× 1 m with only 9 actuators. This scaling comes at a cost: shared actuators mechanically couple neighbouring modules, creating interference during simultaneous manipulation. We systematically characterise this coupling across spatial configurations and propose compensation strategies that reduce passive-object displacement by 59–78%. Together, these contributions establish a scalable foundation for soft manipulation surfaces in applications such as food processing and logistics.

Controlled synthesis of materials with specified atomic structures underpins technological advances yet remains reliant on iterative, trial-and-error approaches. Nanoparticles (NPs), whose atomic arrangement dictates their emergent properties,1-5 are particularly challenging to synthesize due to numerous tunable parameters. Here, we introduce an autonomous approach that explicitly targets atomic-scale structure through scattering patterns. Our method autonomously designs synthesis protocols by matching real-time experimental total scattering (TS) and pair distribution function (PDF) data to simulated target patterns, without requiring embedded synthesis knowledge. We demonstrate this capability at a synchrotron by targeting two structurally distinct gold NP scattering patterns: 5 nm decahedral and 10 nm face-centered cubic structures. Ultimately, specifying target scattering patterns and autonomously approaching synthesis protocols that reproduce them experimentally may enable on-demand, atomic structure-informed materials design. ScatterLab thus provides a generalizable blueprint for autonomous, atomic structure-targeted synthesis across diverse systems and applications.

Easily reusable lab automation is the next frontier in automating experiments. Equipment must be repurposed often for experiments, making the current generation of lab automation too costly and too complicated to apply. However, with the rise of platforms in which modules encapsulate operations, connections and equipment can be swapped around quickly and, in some cases, automatically to perform different experiments. This article reviews the current state of the art of reconfigurable modular laboratory automation systems in chemistry. We discuss the key barriers of adoption of these systems and the importance of standardized benchmarks to compare capabilities of different platforms. We provide an outlook on the most promising research paths, including the possibility that AI systems can help guide the layout of processes and operations, leading to improved efficiency and possibly new process layouts. We conclude that the field of laboratory automation should continue to advance, benefiting chemists and the broader scientific community.

Distributed Manipulator Systems, composed of arrays of robotic actuators necessitate dense actuator arrays to effectively manipulate small objects. This paper presents a system composed of modular 3-DoF robotic tiles interconnected by a compliant surface layer, forming a continuous, controllable manipulation surface. The compliant layer permits increased actuator spacing without compromising object manipulation capabilities, significantly reducing actuator density while maintaining robust control, even for smaller objects. We characterize the coupled workspace of the array and develop a manipulation strategy capable of translating objects to arbitrary positions within an N X N array. The approach is validated experimentally using a minimal 2 X 2 prototype, demonstrating the successful manipulation of objects with varied shapes and sizes.

Biological systems possess remarkable capabilities for self-recognition and morphological regeneration, often relying solely on local interactions. Inspired by these decentralized processes, we present a novel system of physical 3D bricks-simple cubic units equipped with local communication, processing, and sensing-that are capable of inferring their global shape class and detecting structural damage. Leveraging Neural Cellular Automata, a learned, fully-distributed algorithm, our system enables each module to independently execute the same neural network without access to any global state or positioning information. We demonstrate the ability of collections of hundreds of these cellular bricks to accurately classify a variety of 3D shapes through purely local interactions. The approach shows strong robustness to out-of-distribution shape variations and high tolerance to communication faults and failed modules. In addition to shape inference, the same decentralized framework is extended to detect missing or damaged components, allowing the collective to localize structural disruptions and to guide a recovery process. This work provides a physical realization of large-scale, decentralized self-recognition and damage detection, advancing the potential of robust, adaptive, and bio-inspired modular systems.

Object manipulation is a fundamental challenge in robotics, where systems must balance trade-offs among manipulation capabilities, system complexity, and throughput. Distributed manipulator systems (DMS) use the coordinated motion of actuator arrays to perform complex object manipulation tasks, seeing widespread exploration within the literature and in industry. However, existing DMS designs typically rely on high actuator densities and impose constraints on object-to-actuator scale ratios, limiting their adaptability. We present a novel DMS design utilizing an array of 3-DoF, origami-inspired robotic tiles interconnected by a compliant surface layer. Unlike conventional DMS, our approach enables manipulation not only at the actuator end effectors but also across a flexible surface connecting all actuators; creating a continuous, controllable manipulation surface. We analyse the combined workspace of such a system, derive simple motion primitives, and demonstrate its capabilities to translate simple geometric objects across an array of tiles. By leveraging the inter-tile connective material, our approach significantly reduces actuator density, increasing the area over which an object can be manipulated by aproximately X1.84 without an increase in the number of actuators. This design offers a lower cost and complexity alternative to traditional high-density arrays and introduces new opportunities for manipulation strategies that leverage the flexibility of the interconnected surface.

Liquid-liquid extraction (LLE) is an essential operation in many laboratory experiments. However, most automatic LLE devices concentrate on detecting the liquid-liquid interface at one moment in the process, usually at separation, and pay little attention to the state of the liquids as they settle. In this paper, we present an LLE device with a moving optical sensor and light source that move along a vessel instead of the mixture moving relative to the sensor. Analyzing the patterns of light intensity with explainable automatic detection algorithms, the interface can be detected at different positions in the vessel with an error below 2 mm and monitored during the settling process. The device is tested using a mixture of clear oil and water and two extraction steps in a battery interface material synthesis process. Results show that the setup is able to detect interfaces at different positions along the vessel, even with changes in diameter. By monitoring the settling process, we also found that the biggest change in the signal detected occurs around the liquid-liquid interface position, and we also use this information to corroborate it. The recording of sensor measurements at different positions over time can be used to detect different properties of the liquids, which improves control over the process and could also alleviate reproducibility problems in areas of chemistry in which it is costly to repeat procedures.

Object manipulation in robotics faces challenges due to diverse object shapes, sizes, and fragility. Gripper-based methods offer precision and low degrees of freedom (DOF) but the gripper limits the kind of objects to grasp. On the other hand, surface-based approaches provide flexibility for handling fragile and heterogeneous objects but require numerous actuators, increasing complexity. We propose new manipulation hardware that utilizes equally spaced linear actuators placed vertically and connected by a soft surface. In this setup, object manipulation occurs on the soft surface through coordinated movements of the surrounding actuators. This approach requires fewer actuators to cover a large manipulation area, offering a cost-effective solution with a lower DOF compared to dense actuator arrays. It also effectively handles heterogeneous objects of varying shapes and weights, even when they are significantly smaller than the distance between actuators. This method is particularly suitable for managing highly fragile objects in the food industry.

In Distributed Manipulator Systems (DMS), decentralization is a highly desirable property as it promotes robustness and facilitates scalability by distributing computational burden and eliminating singular points of failure. However, current DMS typically utilize a centralized approach to sensing, such as single-camera computer vision systems. This centralization poses a risk to system reliability and offers a significant limiting factor to system size. In this work, we introduce a decentralized approach for sensing in Distributed Manipulator Systems using Neural Cellular Automata (NCA). Demonstrating decentralized sensing in a hardware implementation, we present a novel inductive sensor board designed for distributed sensing and evaluate its ability to estimate global object properties, such as the geometric center, through local interactions and computations. Experiments demonstrate that NCA-based sensing networks accurately estimate object position at 0.24 times the inter-sensor distance. They maintain resilience under sensor faults and noise and scale seamlessly across varying network sizes. These findings underscore the potential of local, decentralized computations to enable scalable, fault-tolerant, and noise-resilient object property estimation in DMS.

Controlled synthesis of materials with specified atomic structures underpins technological advances yet remains reliant on iterative, trial-and-error approaches. Nanoparticles (NPs), whose atomic arrangement dictates their emergent properties, are particularly challenging to synthesise due to numerous tunable parameters. Here, we introduce an autonomous approach explicitly targeting synthesis of atomic-scale structures. Our method autonomously designs synthesis protocols by matching real time experimental total scattering (TS) and pair distribution function (PDF) data to simulated target patterns, without requiring prior synthesis knowledge. We demonstrate this capability at a synchrotron, successfully synthesising two structurally distinct gold NPs: 5 nm decahedral and 10 nm face-centred cubic structures. Ultimately, specifying a simulated target scattering pattern, thus representing a bespoke atomic structure, and obtaining both the synthesised material and its reproducible synthesis protocol on demand may revolutionise materials design. Thus, ScatterLab provides a generalisable blueprint for autonomous, atomic structure-targeted synthesis across diverse systems and applications.

Joint Range of Motion (JROM) development has been shown to facilitate learning motor control in human beings. This developmental strategy has been applied in robotics to improve learning performance with different outcomes: sometimes it is favourable, others irrelevant, and others, even detrimental. The reasons that underpin this variability in the results are still not well understood. In this paper, we seek to better understand the principles underlying the application of JROM based morphological development to make its use more straightforward. To this end, empirical studies were carried out over two representative use cases: quadruped and bipedal robot morphologies learning to walk. Different parameters of the application of JROM development (morphological configuration, JROM developmental strategy, etc.) have been evaluated to elucidate their effects over learning. The results show that there are significant connections between the reduction of the motor space induced by JROM and the way the exploration and exploitation of the solution space is carried out by the learning algorithm, and the performance achieved. Through these connections, we have identified a set of conditions that must be satisfied for JROM development to be effective as a tool for learning improvement.

Manipulation surfaces indirectly control and reposition objects by actively modifying their shape or properties rather than directly gripping objects. These surfaces, equipped with dense actuator arrays, generate dynamic deformations. However, a high-density actuator array introduces considerable complexity due to increased degrees of freedom (DOF), complicating control tasks. High DOF restrict the implementation and utilization of manipulation surfaces in real-world applications as the maintenance and control of such systems exponentially increase with array/surface size. Learning-based control approaches may ease the control complexity, but they require extensive training samples and struggle to generalize for heterogeneous objects. In this study, we introduce a simple, precise and robust PID-based linear close-loop feedback control strategy for heterogeneous object manipulation on MANTA-RAY (Manipulation with Adaptive Non-rigid Textile Actuation with Reduced Actuation density). Our approach employs a geometric transformation-driven PID controller, directly mapping tilt angle control outputs(1D/2D) to actuator commands to eliminate the need for extensive black-box training. We validate the proposed method through simulations and experiments on a physical system, successfully manipulating objects with diverse geometries, weights and textures, including fragile objects like eggs and apples. The outcomes demonstrate that our approach is highly generalized and offers a practical and reliable solution for object manipulation on soft robotic manipulation, facilitating real-world implementation without prohibitive training demands.

In living beings, the natural development of the body has been shown to facilitate learning. The application of these natural developmental principles in robotics have been considered in different robotic morphologies and scenarios, leading to mixed results. Development was found to be beneficial for learning in some instances, but also irrelevant or detrimental in others. This mix of results and scenarios has allowed researchers to extract some notions about the conditions that must be fulfilled or set to apply morphological development successfully. Notions that we have organized to set a series of design conditions to successfully apply morphological development. Thus, in this article, we are going to focus on the study of one of them that has been frequently addressed by researchers in their studies in very general terms. It can be described as the need to achieve a suitable synergy among the different components involved in the development and learning process: morphological development strategy, controller, task, and learning algorithm. In particular, we have concentrated on empirically determining the influence of five developmental strategies, implemented in different ways, applied at different speeds and deployed in different orders and combinations, over the problem of a NAO robot controlled by an artificial neural network obtained through a neuroevolutionary algorithm learning a bipedal walking task. The results obtained permit providing a more detailed description of what a suitable synergy implies and how it can be utilized to design more successful morphological developmental processes to improve robot learning. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

In human beings, the joint development of the body and cognitive system has been shown to facilitate the acquisition of new skills and abilities. In the literature, these natural principles have been applied to robotics with mixed results and different authors have suggested several hypotheses to explain them. One of the most popular hypotheses states that morphological development improves learning by increasing exploration of the solution space, avoiding stagnation in local optima. In this article, we are going to study the influence of growth-based morphological development and its nuances as a tool to improve the exploration of the solution space. We will perform a series of experiments over two different robot morphologies which learn to walk. Furthermore, we will compare these results to another optimization strategy that has been shown to be useful to favor exploration in learning algorithms: the application of noise during learning. Finally, to check if the increased exploration hypothesis holds, we visualize the genotypic space during learning considering the different optimization strategies by using the Search Trajectory Network representation. The results indicate that noise and growth increase exploration, but only growth guides the search towards good solutions.

This work presents a novel sensor setup for the general detection of liquid-liquid interfaces in different mixes of liquids as part of a liquid-liquid extraction device. The sensor setup is applied to a laboratory scale separatory funnel. It uses a near infrared sensor array which receives light going through the liquids inside the funnel, which are illuminated by a light source located on the other side and below the funnel. Light refracts inside the funnel and the liquids and reflects on the interface creating changing patterns in the light intensity measured by the sensor, providing a way of locating the liquid-liquid interface. Liquid mixes with different optical features, from transparent to opaque, emulsion and clean, are used to test whether different types of interfaces produce a distinguishable response on the sensor, allowing to detect interfaces in different situations that can occur as part of an Artificial Intelligence orchestrated battery chemical synthesis process. Emulsion interfaces create a discernible change in the sensor input by lowering the light intensity registered when crossing in front of the sensor making them easier to locate than with other optical techniques. The setup opens the possibility of detecting a liquid-liquid interface as it is forming and can be miniaturized to be attached to laboratory funnels as a manual aid or used with other transparent vessels in automatic solutions like liquid handling robots or pipetting robots.

Materials with the ability to self-classify their own shape have the potential to advance a wide range of engineering applications and industries. Biological systems possess the ability not only to self-reconfigure but also to self-classify themselves to determine a general shape and function. Previous work into modular robotics systems has only enabled self-recognition and self-reconfiguration into a specific target shape, missing the inherent robustness present in nature to self-classify. In this paper we therefore take advantage of recent advances in deep learning and neural cellular automata, and present a simple modular 2D robotic system that can infer its own class of shape through the local communication of its components. Furthermore, we show that our system can be successfully transferred to hardware which thus opens opportunities for future self-classifying machines. Code available at https://github.com/kattwalker/projectcube. Video available at https://youtu.be/0TCOkE4keyc.