Over 100 volunteers sampled meatballs made with the nutritious fruit. Breakthroughs, discoveries, and DIY tips sent every weekday. Finely ground, freeze-dried apple leftovers may become a sustainable secret ingredient in many meat dishes. In recent taste tests at Cornell University, more than 100 volunteers could barely tell the difference between 100-percent pure meatballs and alternatives featuring as much as 20 percent fruit waste. As the food researchers behind this culinary concoction explained in their study published in the, the supplemental additive may also help close a glaring gap in the food industry's circular loop.

Tool use is essential for enabling robots to perform complex real-world tasks, but learning such skills requires extensive datasets. While teleoperation is widely used, it is slow, delay-sensitive, and poorly suited for dynamic tasks. In contrast, human videos provide a natural way for data collection without specialized hardware, though they pose challenges on robot learning due to viewpoint variations and embodiment gaps. To address these challenges, we propose a framework that transfers tool-use knowledge from humans to robots. To improve the policy's robustness to viewpoint variations, we use two RGB cameras to reconstruct 3D scenes and apply Gaussian splatting for novel view synthesis. We reduce the embodiment gap using segmented observations and tool-centric, task-space actions to achieve embodiment-invariant visuomotor policy learning. We demonstrate our framework's effectiveness across a diverse suite of tool-use tasks, where our learned policy shows strong generalization and robustness to human perturbations, camera motion, and robot base movement. Our method achieves a 71\% improvement in task success over teleoperation-based diffusion policies and dramatically reduces data collection time by 77\% and 41\% compared to teleoperation and the state-of-the-art interface, respectively.

Food systems are responsible for a third of human-caused greenhouse gas emissions. We investigate what Large Language Models (LLMs) can contribute to reducing the environmental impacts of food production. We define a typology of design and prediction tasks based on the sustainable food literature and collaboration with domain experts, and evaluate six LLMs on four tasks in our typology. For example, for a sustainable protein design task, food science experts estimated that collaboration with an LLM can reduce time spent by 45% on average, compared to 22% for collaboration with another expert human food scientist. However, for a sustainable menu design task, LLMs produce suboptimal solutions when instructed to consider both human satisfaction and climate impacts. We propose a general framework for integrating LLMs with combinatorial optimization to improve reasoning capabilities. Our approach decreases emissions of food choices by 79% in a hypothetical restaurant while maintaining participants' satisfaction with their set of choices. Our results demonstrate LLMs' potential, supported by optimization techniques, to accelerate sustainable food development and adoption.

Robot-assisted feeding has the potential to improve the quality of life for individuals with mobility limitations who are unable to feed themselves independently. However, there exists a large gap between the homogeneous, curated plates existing feeding systems can handle, and truly in-the-wild meals. Feeding realistic plates is immensely challenging due to the sheer range of food items that a robot may encounter, each requiring specialized manipulation strategies which must be sequenced over a long horizon to feed an entire meal. An assistive feeding system should not only be able to sequence different strategies efficiently in order to feed an entire meal, but also be mindful of user preferences given the personalized nature of the task. We address this with FLAIR, a system for long-horizon feeding which leverages the commonsense and few-shot reasoning capabilities of foundation models, along with a library of parameterized skills, to plan and execute user-preferred and efficient bite sequences. In real-world evaluations across 6 realistic plates, we find that FLAIR can effectively tap into a varied library of skills for efficient food pickup, while adhering to the diverse preferences of 42 participants without mobility limitations as evaluated in a user study. We demonstrate the seamless integration of FLAIR with existing bite transfer methods [19, 28], and deploy it across 2 institutions and 3 robots, illustrating its adaptability. Finally, we illustrate the real-world efficacy of our system by successfully feeding a care recipient with severe mobility limitations. Supplementary materials and videos can be found at: https://emprise.cs.cornell.edu/flair .

The theory of imprecise probabilities [1, 2] is often thought of as a theory of partially specified probabilities, which involves manipulating sets of probabilities and their lower and upper expectations. Its mathematical underpinnings, however, are provided by an underlying theory of sets of desirable gambles [2, 3, 4, 5, 6]: sets of gambles--rewards with an uncertain payoff--that a subject finds desirable, in the sense that she prefers those gambles to the status quo--to the trivial gamble with zero payoff. Rewards are typically taken to be expressed in units of some linear utility scale, and this them implies that positive linear combinations of desirable gambles are desirable themselves. Sets of desirable gambles that satisfy this condition (as well as some other, less essential conditions) are called coherent. Due to the geometric nature of the coherence conditions, inference with desirable gambles is typically simple and intuitive, a feature that is particularly handy, also when it comes to designing proofs. Most crucially, however, well known imprecise probability models such as credal sets (closed convex sets of probabilites), lower and upper expectations (or previsions), partial preference oderings, belief functions and lower and upper probabilities, all correspond to special cases of coherent sets of desirable gambles [4], which explains the importance of the latter as a basis for impreciseprobabilistic reasoning.

If you're one of the 2.4 million Twitter followers of the Hamilton impresario Lin-Manuel Miranda, you've come to expect a delightful stream of observations, including tweets capturing conversations with his son Sebastian, now 3 years old. Earlier this month, Miranda offered one such exchange under the title, "S'MORES. Me: So that's the marshmallow but you're going to eat it with this graham cracker and chocolate. Sebastian: No, I'm going to eat it with my MOUTH. A charming slice of life, to be sure. But in that brief interaction, young Sebastian Miranda also inadvertently hit upon a kind of ambiguity that reveals a great deal about how people learn and process language--and how we might teach computers to do the same. The misinterpretation on which the s'mores story hinges is hiding in the humble preposition with. I'm going to eat this marshmallow with ... If you're in the mood for s'mores, then "graham cracker and chocolate" is an appropriate object of the preposition with.