Many current robot designs prioritize efficiency and one-size-fits-all solutions, oftentimes overlooking personalization, adaptability, and sustainability. To explore alternatives, we conducted two co-design workshops with 23 participants, who engaged with a modular robot co-design framework. Using components we provided as building blocks, participants combined, removed, and invented modules to envision how modular robots could accompany them from childhood through adulthood and into older adulthood. The participants’ designs illustrate how modularity (a) enables personalization through open-ended configuration, (b) adaptability across shifting life-stage needs, and (c) sustainability through repair, reuse, and continuity. We therefore derive design principles that establish modularity as a foundation for lifespan-oriented human–robot interaction. This work reframes modular robotics as a flexible and expressive co-design approach, supporting robots that evolve with people, rather than static products optimized for single moments or contexts of use.

Robotics has emerged as a critical field of technological innovation. However, current design paradigms, rooted in industrial-era models, often prioritize centralized control, planned obsolescence, and rigid, one-size-fits-all solutions, undermining adaptability, sustainability, and personal autonomy. To address these limitations, we propose a speculative robotic design framework rooted in Sustainability, Adaptability, and Modularity. Our framework envisions robots as modular systems that can be assembled, reconfigured, and personalized by people, shifting design control away from centralized decision-makers and enabling long-term usability. Modularity enables adaptability and reduces obsolescence, while adaptability reinforces sustainability through circular resource use and extended lifecycles. This speculative framework not only provides a technical vision but also reimagines robotic design as a participatory and collaborative process aligned with ecological responsibility. Our speculative framework offers a manifesto for systemic change, reshaping robotics for a more equitable, sustainable future between humans, robots, and ecology.

Many modern machine learning (ML) methods rely on embedding models to learn vector representations (embeddings) for a set of entities (embedding tables). As increasingly diverse ML applications utilize embedding models and embedding tables continue to grow in size and number, there has been a surge in the ad-hoc development of specialized frameworks targeted to train large embedding models for specific tasks. Although the scalability issues that arise in different embedding model training tasks are similar, each of these frameworks independently reinvents and customizes storage components for specific tasks, leading to substantial duplicated engineering efforts in both development and deployment. This paper presents MLKV, an efficient, extensible, and reusable data storage framework designed to address the scalability challenges in embedding model training, specifically data stall and staleness. MLKV augments disk-based key-value storage by democratizing optimizations that were previously exclusive to individual specialized frameworks and provides easy-to-use interfaces for embedding model training tasks. Extensive experiments on open-source workloads, as well as applications in eBay's payment transaction risk detection and seller payment risk detection, show that MLKV outperforms offloading strategies built on top of industrial-strength key-value stores by 1.6-12.6 ×. MLKV is open-source at https://github.com/llm-db/MLKV.

Reducing electronic waste is one of the key topics in Sustainable Interaction Design. However, research regarding the sustainable future of the long-term use of robots is limited. Our study employs a game-based workshop to investigate the factors influencing potential users’ sustainability choices in long-term human-robot interactions. We developed a board game called “Game of Life with Your Companion Robot” to help participants situate themselves in the context of cohabiting with companion robots of their choice. Through five workshops with seventeen participants, we explore (a) the factors mentioned by participants that influence their sustainability choices in long-term human-robot interactions, and (b) the connections between how participants frame their companion robots and their sustainability choices. We use four sustainable criteria to evaluate participants’ choices. Our findings show that different framings of robots can result in different sustainable outcomes.

There are currently multiple proposed security label designs for consumer products, with each prioritizing different security and privacy factors. These differences risk making product comparisons more confusing than informative. Standardized labels could potentially resolve this by informing consumers of a product's security features at the point of purchase. But which standard? This survey, of 500 participants, studied four label designs and measured comprehension, response time, acceptability, and cognitive load. We gauged understanding of participant perception and preferences using three smart devices: light bulbs, cameras, and thermostats. We identified preferences and behaviors before, during, and after label use for product selection. At first, participants believed more information-dense labels would better support their purchasing behavior; however, after they evaluated and compared products, participants gravitated towards less cognitively demanding designs. We identified how participants utilized and prioritized label elements to provide recommendations for US label design efforts.

To handle graphs in which features or connections are evolving over time, a series of temporal graph neural networks (TGNNs) have been proposed. Despite the success of these TGNNs, the previous TGNN evaluations reveal several limitations regarding four critical issues: 1) inconsistent datasets, 2) inconsistent evaluation pipelines, 3) lacking workload diversity, and 4) lacking efficient comparison. Overall, there lacks an empirical study that puts TGNN models onto the same ground and compares them comprehensively. To this end, we propose Benchtemp, a general benchmark for evaluating TGNN models on various workloads. Benchtemp provides a set of benchmark datasets so that different TGNN models can be fairly compared. Further, Benchtemp engineers a standard pipeline that unifies the TGNN evaluation. With Benchtemp, we extensively compare the representative TGNN models on different tasks (e.g., link prediction and node classification) and settings (transductive and inductive), w.r.t. both effectiveness and efficiency metrics. We have made Benchtemp publicly available at https://github.com/qianghuangwhu/benchtemp and datasets at https://zenodo.org/record/8267846.

Balancing oneself using the spine is a physiological alignment of the body posture in the most efficient manner by the muscular forces for mammals. For this reason, we can see many disabled quadruped animals can still stand or walk even with three limbs. This paper investigates the optimization of dynamic balance during trot gait based on the spatial relationship between the center of mass (CoM) and support area influenced by spinal flexion. During trotting, the robot balance is significantly influenced by the distance of the CoM to the support area formed by diagonal footholds. In this context, lateral spinal flexion, which is able to modify the position of footholds, holds promise for optimizing balance during trotting. This paper explores this phenomenon using a rat robot equipped with a soft actuated spine. Based on the lateral flexion of the spine, we establish a kinematic model to quantify the impact of spinal flexion on robot balance during trot gait. Subsequently, we develop an optimized controller for spinal flexion, designed to enhance balance without altering the leg locomotion. The effectiveness of our proposed controller is evaluated through extensive simulations and physical experiments conducted on a rat robot. Compared to both a non-spine based trot gait controller and a trot gait controller with lateral spinal flexion, our proposed optimized controller effectively improves the dynamic balance of the robot and retains the desired locomotion during trotting.

This article investigates the robust optimal consensus for nonlinear multiagent systems (MASs) through the local adaptive dynamic programming (ADP) approach and the event-triggered control method. Due to the nonlinearities in dynamics, the first part defines a novel measurement error to construct a distributed integral sliding-mode controller, and the consensus errors can approximately converge to the origin in a fixed time. Then, a modified cost function with augmented control is proposed to deal with the unmatched disturbances for the event-based optimal consensus controller. Specifically, a single network local ADP structure with novel concurrent learning is presented to approximate the optimal consensus policies, which guarantees the robustness of the MASs and the uniform ultimate boundedness (UUB) of the neural network (NN) weights' estimation error and relaxes the requirement of initial admissible control. Finally, an illustrative simulation verifies the effectiveness of the method.

The aim of this research is to investigate the relationship between spinal flexion and quadruped locomotion in a rat robot equipped with a compliant spine, controlled by a central pattern generator (CPG). The study reveals that spinal flexion can enhance limb stride length, but it may also cause significant and unexpected motion disturbances during stride length variations. To address this issue, this paper proposes a CPG model driven by spinal flexion and a novel oscillator that incorporates a circular limit cycle and accounts for the anticipated stride length transition process. This approach effectively matches the torque change with the dynamics of stride length changes, leading to lower energy consumption. Extensive simulations are conducted to evaluate the efficacy of the proposed oscillator and compare it with the original kinetic model and other CPG models. The results demonstrate that the designed CPG model with the proposed oscillator yields smoother gait transitions during stride length variations and reduces energy consumption.

Detecting fraudulent transactions is an essential component to control risk in e-commerce marketplaces. Apart from rule-based and machine learning filters that are already deployed in production, we want to enable efficient real-time inference with graph neural networks (GNNs), which is useful to catch multihop risk propagation in a transaction graph. However, two challenges arise in the implementation of GNNs in production. First, future information in a dynamic graph should not be considered in message passing to predict the past. Second, the latency of graph query and GNN model inference is usually up to hundreds of milliseconds, which is costly for some critical online services. To tackle these challenges, we propose a Batch and Real-time Inception GrapH Topology (BRIGHT) framework to conduct an end-to-end GNN learning that allows efficient online real-time inference.

In nature, the movement of quadrupeds is completed under the combined action of the spine and the legs. Inspired by this, this paper explores the effect of a lateral flexing spine on the locomotion of a rat robot. Benefiting from the regular lateral flexion of a soft actuated spine, the rat robot exhibits enhance step length of its hind legs and increased translational velocity by coordinating the opposite movements of the left and right sides. Furthermore, this paper introduces a mathematical model of the effect of the flexible spine on the robot velocity. Finally, extensive experiments are conducted in simulations and on the physical rat robot. Compared with the locomotion without a flexing spine, the simulation results show that the velocity of the robot can be increased up to 218.29%, which is in line with the theoretical results from the proposed mathematical model. Limited by the gap between simulation and the real world, the experiment results of the physical rat robot show a slight performance than the theoretical results. But the physical rat robot can still enhance its translational velocity with the help of a lateral flexing spine.

Mining from graph-structured data is an integral component of graph data management. A recent trending technique, graph convolutional network (GCN), has gained momentum in the graph mining field, and plays an essential part in numerous graph-related tasks. Although the emerging GCN optimization techniques bring improvements to specific scenarios, they perform diversely in different applications and introduce many trial-and-error costs for practitioners. Moreover, existing GCN models often suffer from oversmoothing problem. Besides, the entanglement of various graph patterns could lead to non-robustness and harm the final performance of GCNs. In this work, we propose a simple yet efficient graph decomposition approach to improve the performance of general graph neural networks. We first empirically study existing graph decomposition methods and propose an automatic connectivity-ware graph decomposition algorithm, DeGNN. To provide a theoretical explanation, we then characterize GCN from the information-theoretic perspective and show that under certain conditions, the mutual information between the output after l layers and the input of GCN converges to 0 exponentially with respect to l. On the other hand, we show that graph decomposition can potentially weaken the condition of such convergence rate, alleviating the information loss when GCN becomes deeper. Extensive experiments on various academic benchmarks and real-world production datasets demonstrate that graph decomposition generally boosts the performance of GNN models. Moreover, our proposed solution DeGNN achieves state-of-the-art performances on almost all these tasks.

At online retail platforms, it is crucial to actively detect the risks of transactions to improve customer experience and minimize financial loss. In this work, we propose xFraud, an explainable fraud transaction prediction framework which is mainly composed of a detector and an explainer. The xFraud detector can effectively and efficiently predict the legitimacy of incoming transactions. Specifically, it utilizes a heterogeneous graph neural network to learn expressive representations from the informative heterogeneously typed entities in the transaction logs. The explainer in xFraud can generate meaningful and human-understandable explanations from graphs to facilitate further processes in the business unit. In our experiments with xFraud on real transaction networks with up to 1.1 billion nodes and 3.7 billion edges, xFraud is able to outperform various baseline models in many evaluation metrics while remaining scalable in distributed settings. In addition, we show that xFraud explainer can generate reasonable explanations to significantly assist the business analysis via both quantitative and qualitative evaluations.

This paper presents the design rationale and concept development behind MemoryReel, a tangibly interactive desktop device that records special moments of online social interactions, between couples and friends over a long distance, and supports later reminiscence. In a human-centered design process started with a two-act design inquiry, we developed the design concept and implemented a medium-to-high-fidelity interactive prototype. We then invited 20 participants to experience the prototype and give comments. The design rationale together with the analysis of the exploratory user study findings provides insights on the dimensions and strategies of a design space for digital memories and long-distance relationships, with an emphasis on reminiscence support.