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.

Many children experience challenges in emotional regulation and social interaction, which can limit their participation in everyday activities and therapeutic programs. For socially assistive robots to be effective in this context, it is essential that children remain consistently and meaningfully engaged. We explore engagement strategies for a tactile robot designed to support children suffering from anxiety disorders through daily interactions. The robot delivers either synthetic emotional feedback or point rewards to encourage user participation. We evaluated these strategies through two studies: a preference assessment with 16 school children aged 6-8 years, and a behavioral study with 14 university students aged 20-27 years in naturalistic environments. The study with school children indicated a preference for emotional engagement over points-based approaches. The follow up study with university students across a full day of interactions revealed contrasting results: points-based systems produced significantly higher task accuracy (p < 0.05) and sustained performance over time. Findings from different user groups suggest that stated preferences and behavioral outcomes can diverge depending on engagement context, highlighting the importance of validating design assumptions through observed interaction. This work contributes insights into age-related differences in engagement strategy effectiveness in human-robot interaction design.

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.

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.

Periods of heightened arousal or restlessness can interfere with children's ability to focus, self-regulation, and physically calm. Technologies that encourage embodied self-regulation through tactile interaction may provide a simple and accessible means of promoting calmness. This paper investigates how interaction with a pocket-sized tactile device influences physiological and behavioral markers of calmness in typically developing children. Building on prior work examining heart rate modulation, we present new findings on how tactile interaction affects full-body movement and postural stability. We employ a device that engages children through a hand-held rhythmic vibration-matching game, designed to focus attention and encourage stillness. Eighteen children participated in a within-subjects study that involved two conditions: with and without tactile interaction with a hand-held device, while having their heart rate and body movement recorded. Results show that the tactile game interaction reduced physiological arousal (heart rate decreased by 3.56 bpm, p < 0.01) and physical restlessness (overall movement decreased by 38

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.

This paper proposes a self-organizing decentralized control framework for modular robotic manipulation surfaces composed of locally interacting actuators. These surfaces aim to induce object translation and rotation using only local sensing and actuation, without centralized coordination or global object tracking. We introduce and systematically compare four behavior rule variants–Discrete, Logistic, Gaussian, and Fourier–under the umbrella of Swarm-Inspired Controllers. Through simulation experiments on various 2D object shapes, we evaluate positioning accuracy, orientation alignment, operation time, and robustness to actuator failure. Results show that decentralized behavior achieves positioning, orientation, and fault tolerance comparable to a centralized baseline, despite relying solely on local information. This demonstrates that fully decentralized heuristics can match centralized control in effectiveness, while offering scalability and resilience.

Children diagnosed with anxiety disorders are taught a range of strategies to navigate situations of heightened anxiety. Techniques such as deep breathing and repetition of mantras are commonly employed, as they are known to be calming and reduce elevated heart rates. Although these strategies are often effective, their successful application relies on prior training of the children for successful use when faced with challenging situations. This paper investigates a pocket-sized companion robot designed to offer a relaxation technique requiring no prior training, with a focus on immediate impact on the user's heart rate. The robot utilizes a tactile game to divert the user's attention, thereby promoting relaxation. We conducted two studies with children who were not diagnosed with anxiety: a 14-day pilot study with two children (age 8) and a main study with 18 children (ages 7-8). Both studies employed a within-subjects design and focused on measuring heart rate during tactile interaction with the robot and during non-use. Interacting with the robot was found to significantly lower the study participants' heart rate (p<0.01) compared to the non-use condition, indicating a consistent calming effect across all participants. These results suggest that tactile companion robots have the potential to enhance the therapeutic value of relaxation techniques.

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.

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.

This paper presents Affecta-context, a general framework to facilitate behavior adaptation for social robots. The framework uses information about the physical context to guide its behaviors in human-robot interactions. It consists of two parts: one that represents encountered contexts and one that learns to prioritize between behaviors through human-robot interactions. As physical contexts are encountered the framework clusters them by their measured physical properties. In each context, the framework learns to prioritize between behaviors to optimize the physical attributes of the robot's behavior in line with its current environment and the preferences of the users it interacts with. This paper illlustrates the abilities of the Affecta-context framework by enabling a robot to autonomously learn the prioritization of discrete behaviors. This was achieved by training across 72 interactions in two different physical contexts with 6 different human test participants. The paper demonstrates the trained Affecta-context framework by verifying the robot's ability to generalize over the input and to match its behaviors to a previously unvisited physical context.

A common denominator for most therapy treatments for children who suffer from an anxiety disorder is daily practice routines to learn techniques needed to overcome anxiety. However, applying those techniques while experiencing anxiety can be highly challenging. This paper presents the design, implementation, and pilot study of a tactile hand-held pocket robot “AffectaPocket”, designed to work alongside therapy as a focus object to facilitate coping during an anxiety attack. The robot does not require daily practice to be used, has a small form factor, and has been designed for children 7 to 12 years old. The pocket robot works by sensing when it is being held and attempts to shift the child's focus by presenting them with a simple three-note rhythm-matching game. We conducted a pilot study of the pocket robot involving four children aged 7 to 10 years, and then a main study with 18 children aged 6 to 8 years; neither study involved children with anxiety. Both studies aimed to assess the reliability of the robot's sensor configuration, its design, and the effectiveness of the user tutorial. The results indicate that the morphology and sensor setup performed adequately and the tutorial process enabled the children to use the robot with little practice. This work demonstrates that the presented pocket robot could represent a step toward developing low-cost accessible technologies to help children suffering from anxiety disorders.

This paper introduces a self-organizing decentralized controller employing an information diffusion mechanism to govern the behavior of surface-based distributed manipulators. These systems utilize independently controlled actuators arranged in a grid for precise positioning and orientation of objects. The proposed approach is demonstrated in a simulated virtual environment with a generic distributed manipulator. The system's self-organization capabilities are evaluated through experiments involving objects of varying sizes and shapes. The results show the robustness, fault tolerance, and performance of the system. The approach's high level of abstraction makes it versatile for different actuation principles and sensing devices, focusing on the essential information and module capabilities needed for the task.

Distributed manipulation systems employ a grid of independently controlled actuators to achieve precise manipulation of objects resting on their surface. Despite the decentralized nature of the actuators, current implementations use centralized feedback mechanisms to provide information about the object's position to the controllers. This centralized approach introduces a potential vulnerability, as a failure in the feedback system could result in the complete failure of the system. This paper proposes an approach for characterizing objects in decentralized systems. Its validation is demonstrated through the implementation of a two-dimensional simulated network of sensing agents. These agents work collaboratively to determine a global property - the geometrical center of an object - through local communication of the information at their disposal. The method uses a Neural Cellular Automaton, a multi-agent system in which the update rule of each agent is expressed as a Neural Network, and it is a function of its neighborhood's information. Experiments show that trained on known objects, the system displayed outstanding precision in inferring their centers and demonstrated remarkable adaptability when encountering unknown objects.