One of the main challenges in mechanism design is to carefully engineer incentives ensuring truthfulness while maintaining strong social welfare approximation guarantees. But these objectives are often in conflict, making it impossible to design effective mechanisms. An important class of mechanism design problems that belong to this category are budget-feasible mechanisms. Here, the designer needs to procure services of maximum value from a set of agents while being on a budget, i.e., having a limited budget to enforce truthfulness. However, as empirical studies suggest, factors like limited information and bounded rationality question the idealized assumption that the agents behave perfectly rationally. Motivated by this, Troyan and Morill in 2022 introduced non-obvious manipulability (NOM) as a more lenient incentive compatibility notion. In this paper, we investigate whether resorting to NOM enables us to derive improved mechanisms in budget-feasible domains. We establish a tight bound of 2 on the approximation guarantee of budget-feasible mechanisms satisfying NOM for the general class of monotone subadditive valuation functions. Our result thus establishes a clear separation
Accurate dietary monitoring is essential for promoting healthier eating habits. A key area of research is how people interact and consume food using utensils and hands. By tracking their position and orientation, it is possible to estimate the volume of food being consumed, or monitor eating behaviours, highly useful insights into nutritional intake that can be more reliable than popular methods such as self-reporting. Hence, this paper implements a system that analyzes stationary video feed of people eating, using 6D pose estimation to track hand and spoon movements to capture spatial position and orientation. In doing so, we examine the performance of two state-of-the-art (SOTA) video object segmentation (VOS) models, both quantitatively and qualitatively, and identify main sources of error within the system.
Monitoring dietary intake is a crucial aspect of promoting healthy living. In recent years, advances in computer vision technology have facilitated dietary intake monitoring through the use of images and depth cameras. However, the current state-of-the-art image-based food portion estimation algorithms assume that users take images of their meals one or two times, which can be inconvenient and fail to capture food items that are not visible from a top-down perspective, such as ingredients submerged in a stew. To address these limitations, we introduce an innovative solution that utilizes stationary user-facing cameras to track food items on utensils, not requiring any change of camera perspective after installation. The shallow depth of utensils provides a more favorable angle for capturing food items, and tracking them on the utensil's surface offers a significantly more accurate estimation of dietary intake without the need for post-meal image capture. The system is reliable for estimation of nutritional content of liquid-solid heterogeneous mixtures such as soups and stews. Through a series of experiments, we demonstrate the exceptional potential of our method as a non-invasive,
Eating is a daily challenge for over 60 million adults with essential tremors and other mobility limitations. For these users, traditional utensils like forks or spoons are difficult to manipulate -- resulting in accidental spills and restricting the types of food that can be consumed. Prior work has developed rigid, hand-held utensils that often fail to secure food, as well as soft, shape-changing utensils made strictly for robot-assisted feeding. To assist a broader range of users, we introduce a re-designed kiri-spoon that can be leveraged as either a hand-held utensil or a robot-mounted attachment. Our key idea -- developed in collaboration with stakeholders -- is a pivot-based design. With this design the kiri-spoon behaves like a pair of pliers: users squeeze the handles to change the shape of the utensil and enclose food morsels. In practice, users can apply this kiri-spoon as either a spoon (that scoops food) or as a fork (that pinches food); when the handles are closed, the utensil wraps around the morsel and prevents it from accidentally falling. We characterize the amount of force required to open or close the kiri-spoon, and show how designers can modify this force thro
Scooping items with tools such as spoons and ladles is common in daily life, ranging from assistive feeding to retrieving items from environmental disaster sites. However, developing a general and autonomous robotic scooping policy is challenging since it requires reasoning about complex tool-object interactions. Furthermore, scooping often involves manipulating deformable objects, such as granular media or liquids, which is challenging due to their infinite-dimensional configuration spaces and complex dynamics. We propose a method, SCOOP'D, which uses simulation from OmniGibson (built on NVIDIA Omniverse) to collect scooping demonstrations using algorithmic procedures that rely on privileged state information. Then, we use generative policies via diffusion to imitate demonstrations from observational input. We directly apply the learned policy in diverse real-world scenarios, testing its performance on various item quantities, item characteristics, and container types. In zero-shot deployment, our method demonstrates promising results across 465 trials in diverse scenarios, including objects of different difficulty levels that we categorize as "Level 1" and "Level 2." SCOOP'D outp
In this paper, we explore the use of a soft gripper, specifically a soft inverting-everting toroidal hydrostat, as a prosthetic hand. We present a design of the gripper integrated into a body-powered elbow-driven system and evaluate its performance compared to similar body-powered terminal devices: the Kwawu 3D-printed hand and the Hosmer hook. Our experiments highlight advantages of the Everting hand, such as low required cable tension for operation (1.6 N for Everting, 30.0 N for Kwawu, 28.1 N for Hosmer), limited restriction on the elbow angle range, and secure grasping capability (peak pulling force required to remove an object: 15.8 N for Everting, 6.9 N for Kwawu, 4.0 N for Hosmer). In our pilot user study, six able-bodied participants performed standardized hand dexterity tests. With the Everting hand compared to the Kwawu hand, users transferred more blocks in one minute and completed three tasks (moving small common objects, simulated feeding with a spoon, and moving large empty cans) faster (p~$\leq$~0.05). With the Everting hand compared to the Hosmer hook, users moved large empty cans faster (p~$\leq$~0.05) and achieved similar performance on all other tasks. Overall, u
Effectively integrating diverse sensory modalities is crucial for robotic manipulation. However, the typical approach of feature concatenation is often suboptimal: dominant modalities such as vision can overwhelm sparse but critical signals like touch in contact-rich tasks, and monolithic architectures cannot flexibly incorporate new or missing modalities without retraining. Our method factorizes the policy into a set of diffusion models, each specialized for a single representation (e.g., vision or touch), and employs a router network that learns consensus weights to adaptively combine their contributions, enabling incremental of new representations. We evaluate our approach on simulated manipulation tasks in {RLBench}, as well as real-world tasks such as occluded object picking, in-hand spoon reorientation, and puzzle insertion, where it significantly outperforms feature-concatenation baselines on scenarios requiring multimodal reasoning. Our policy further demonstrates robustness to physical perturbations and sensor corruption. We further conduct perturbation-based importance analysis, which reveals adaptive shifts between modalities.
The remote embodied referring expression (REVERIE) task requires an agent to navigate through complex indoor environments and localize a remote object specified by high-level instructions, such as "bring me a spoon", without pre-exploration. Hence, an efficient navigation plan is essential for the final success. This paper proposes a novel parameter-efficient action planner using large language models (PEAP-LLM) to generate a single-step instruction at each location. The proposed model consists of two modules, LLM goal planner (LGP) and LoRA action planner (LAP). Initially, LGP extracts the goal-oriented plan from REVERIE instructions, including the target object and room. Then, LAP generates a single-step instruction with the goal-oriented plan, high-level instruction, and current visual observation as input. PEAP-LLM enables the embodied agent to interact with LAP as the path planner on the fly. A simple direct application of LLMs hardly achieves good performance. Also, existing hard-prompt-based methods are error-prone in complicated scenarios and need human intervention. To address these issues and prevent the LLM from generating hallucinations and biased information, we propos
For millions of adults with mobility limitations, eating meals is a daily challenge. A variety of robotic systems have been developed to address this societal need. Unfortunately, end-user adoption of robot-assisted feeding is limited, in part because existing devices are unable to seamlessly grasp, manipulate, and feed diverse foods. Recent works seek to address this issue by creating new algorithms for food acquisition and bite transfer. In parallel to these algorithmic developments, however, we hypothesize that mechanical intelligence will make it fundamentally easier for robot arms to feed humans. We therefore propose Kiri-Spoon, a soft utensil specifically designed for robot-assisted feeding. Kiri-Spoon consists of a spoon-shaped kirigami structure: when actuated, the kirigami sheet deforms into a bowl of increasing curvature. Robot arms equipped with Kiri-Spoon can leverage the kirigami structure to wrap-around morsels during acquisition, contain those items as the robot moves, and then compliantly release the food into the user's mouth. Overall, Kiri-Spoon combines the familiar and comfortable shape of a standard spoon with the increased capabilities of soft robotic grippers
The present paper is devoted to comprehensive theoretical studies of exction-polariton quantum fluids specificities in the optics of their utilization for quantum turbulence research. We show that a non-trivial implementation of time-varying potential for excitation of quantum fluid (injection of quantized vortices) via the stirring procedure can be efficiently substituted with resonant excitation-based phase-imprinting techniques. The most efficient phase pattern corresponds to imprinting of tiles with randomly oriented plane waves in each. The resulting turbulent flows, spatial vortex distributions, and clustering statistics resemble those for the case of a conventional spoon-stirring scheme. We quantify the limitations on the lifetime and density depletion for the development and sustainability of quantum turbulence. The yield is the necessity to prevent the density depletion for more than one order of magnitude. Finally, we demonstrate that turbulence is robust with respect to alternating gain and loss at a certain range of modulation parameters, which corresponds to laser operating above and below condensation threshold.
The introduction of Grationality at a 2025 sectional meeting of the Mathematical Association of America installed a handle on a concept akin to rationality of numbers, but in a geometric context. A nice $n$-gon was defined to be a regular $n$-gon with side lengths that are natural numbers, and a number $n$ was defined to be grational if and only if there exists a nice $n$-gon such that its area equals the sum of areas of $n$ congruent nice $n$-gons. This paper shows several examples of grational and non-grational numbers, followed by theorems about how the grationality of a number relates to its divisibility. Proofs of these theorems do not use high-powered tools, but rely on geometric constructions, proportional reasoning, tiling, dissection, the Carpets Theorem, and proof by descent. In keeping with this simplicity, a.k.a. "doing math with a spoon," images are heavily leveraged. The benefits of choosing simplistic tools are discussed.
Bounded rationality in mechanism design aims to ensure incentive-compatibility for agents who are cognitively limited. These agents lack the contingent reasoning skills that traditional mechanism design assumes, and depending on how these cognitive limitations are modelled this alters the class of incentive-compatible mechanisms. In this work we design mechanisms without any "obvious" manipulations for several auction settings that aim to either maximise revenue or minimise the compensation paid to the agents. A mechanism without obvious manipulations is said to be "not obviously manipulable" (NOM), and assumes agents act truthfully as long as the maximum and minimum utilities from doing so are no worse than the maximum and minimum utilities from lying, with the extremes taken over all possible actions of the other agents. We exploit the definition of NOM by introducing the concept of "golden tickets" and "wooden spoons", which designate bid profiles ensuring the mechanism's incentive-compatibility for each agent. We then characterise these "Willy Wonka" mechanisms, and by carefully choosing the golden tickets and wooden spoons we use this to design revenue-maximising auctions and
Can a model distinguish between the sound of a spoon hitting a hardwood floor versus a carpeted one? Everyday object interactions produce sounds unique to the objects involved. We introduce the sounding object detection task to evaluate a model's ability to link these sounds to the objects directly involved. Inspired by human perception, our multimodal object-aware framework learns from in-the-wild egocentric videos. To encourage an object-centric approach, we first develop an automatic pipeline to compute segmentation masks of the objects involved to guide the model's focus during training towards the most informative regions of the interaction. A slot attention visual encoder is used to further enforce an object prior. We demonstrate state of the art performance on our new task along with existing multimodal action understanding tasks.
Open-vocabulary 3D scene understanding is crucial for applications requiring natural language-driven spatial interpretation, such as robotics and augmented reality. While 3D Gaussian Splatting (3DGS) offers a powerful representation for scene reconstruction, integrating it with open-vocabulary frameworks reveals a key challenge: cross-view granularity inconsistency. This issue, stemming from 2D segmentation methods like SAM, results in inconsistent object segmentations across views (e.g., a "coffee set" segmented as a single entity in one view but as "cup + coffee + spoon" in another). Existing 3DGS-based methods often rely on isolated per-Gaussian feature learning, neglecting the spatial context needed for cohesive object reasoning, leading to fragmented representations. We propose Context-Aware Gaussian Splatting (CAGS), a novel framework that addresses this challenge by incorporating spatial context into 3DGS. CAGS constructs local graphs to propagate contextual features across Gaussians, reducing noise from inconsistent granularity, employs mask-centric contrastive learning to smooth SAM-derived features across views, and leverages a precomputation strategy to reduce computatio
SODA aims to revolutionize assistive feeding systems by designing a multi-purpose utensil using origami-inspired artificial muscles. Traditional utensils, such as forks and spoons,are hard and stiff, causing discomfort and fear among users, especially when operated by autonomous robotic arms. Additionally, these systems require frequent utensil changes to handle different food types. Our innovative utensil design addresses these issues by offering a versatile, adaptive solution that can seamlessly transition between gripping and scooping various foods without the need for manual intervention. Utilizing the flexibility and strength of origami-inspired artificial muscles, the utensil ensures safe and comfortable interactions, enhancing user experience and efficiency. This approach not only simplifies the feeding process but also promotes greater independence for individuals with limited mobility, contributing to the advancement of soft robotics in healthcare applications.
Assistive robot arms have the potential to help disabled or elderly adults eat everyday meals without relying on a caregiver. To provide meaningful assistance, these robots must reach for food items, pick them up, and then carry them to the human's mouth. Current work equips robot arms with standard utensils (e.g., forks and spoons). But -- although these utensils are intuitive for humans -- they are not easy for robots to control. If the robot arm does not carefully and precisely orchestrate its motion, food items may fall out of a spoon or slide off of the fork. Accordingly, in this paper we design, model, and test Kiri-Spoon, a novel utensil specifically intended for robot-assisted feeding. Kiri-Spoon combines the familiar shape of traditional utensils with the capabilities of soft grippers. By actuating a kirigami structure the robot can rapidly adjust the curvature of Kiri-Spoon: at one extreme the utensil wraps around food items to make them easier for the robot to pick up and carry, and at the other extreme the utensil returns to a typical spoon shape so that human users can easily take a bite of food. Our studies with able-bodied human operators suggest that robot arms equi
Socioeconomic bias in society exacerbates disparities, influencing access to opportunities and resources based on individuals' economic and social backgrounds. This pervasive issue perpetuates systemic inequalities, hindering the pursuit of inclusive progress as a society. In this paper, we investigate the presence of socioeconomic bias, if any, in large language models. To this end, we introduce a novel dataset SilverSpoon, consisting of 3000 samples that illustrate hypothetical scenarios that involve underprivileged people performing ethically ambiguous actions due to their circumstances, and ask whether the action is ethically justified. Further, this dataset has a dual-labeling scheme and has been annotated by people belonging to both ends of the socioeconomic spectrum. Using SilverSpoon, we evaluate the degree of socioeconomic bias expressed in large language models and the variation of this degree as a function of model size. We also perform qualitative analysis to analyze the nature of this bias. Our analysis reveals that while humans disagree on which situations require empathy toward the underprivileged, most large language models are unable to empathize with the socioecon
We study in this article the motion of a floating ball attached to a soft string set in circular motion through its other end. Although simple, the system exhibits rich dynamics that we investigate experimentally and theoretically. At low rotation speeds, we show that the circular trajectory of the ball shrinks when we stir faster, which challenges common intuition based on centrifugal force. For higher rotation rates, the ball is either suddenly attracted toward the center, or is repulsed away from it, depending on the string length. Experimental measurements of the generated flow show that a Magnus force must be taken into account to correctly explain all the observations. In particular, our deformable system allows to measure the ratio of the lift force over the inertial force. Interestingly, the system exhibits strong hysteretic behavior, showing that the ball can robustly trap itself at the center of the flow generated by its past motion. The present experiment also revisits the famous 'tea-leaf paradox', which refers to the unexpected migration of tea leaves toward the center of a tea cup when the latter is mixed with a rigid spoon. The ball attached to the string plays the r
Humans learn about objects via interaction and using multiple perceptions, such as vision, sound, and touch. While vision can provide information about an object's appearance, non-visual sensors, such as audio and haptics, can provide information about its intrinsic properties, such as weight, temperature, hardness, and the object's sound. Using tools to interact with objects can reveal additional object properties that are otherwise hidden (e.g., knives and spoons can be used to examine the properties of food, including its texture and consistency). Robots can use tools to interact with objects and gather information about their implicit properties via non-visual sensors. However, a robot's model for recognizing objects using a tool-mediated behavior does not generalize to a new tool or behavior due to differing observed data distributions. To address this challenge, we propose a framework to enable robots to transfer implicit knowledge about granular objects across different tools and behaviors. The proposed approach learns a shared latent space from multiple robots' contexts produced by respective sensory data while interacting with objects using tools. We collected a dataset us
Here, I present the community with exoTEDRF (EXOplanet Transit and Eclipse Data Reduction Framework; formerly known as supreme-SPOON), an end-to-end pipeline for data reduction and light curve analysis of time series observations (TSOs) of transiting exoplanets with JWST. The pipeline is highly modular and designed to produce reliable spectra from raw JWST exposures. exoTEDRF (pronounced exo-tedorf) consists of four stages, each of which are further subdivided into a series of steps. These steps can either be run individually, for example in a Jupyter notebook, or via the command line using the provided configuration files. The steps are highly tunable, allowing full control over every parameter in the reduction. Each step also produces diagnostic plots to allow the user to verify their results at each intermediate stage, and compare outputs with other pipelines if so desired. Finally, exoTEDRF has also been designed to be run in "batch" mode: simultaneously running multiple reductions, each tweaking a subset of parameters, to understand any impacts on the resulting atmosphere spectrum.