Card games are widely used to study sequential decision-making under uncertainty, with real-world analogues in negotiation, finance, and cybersecurity. These games typically fall into three categories based on the flow of control: strictly sequential (players alternate single actions), deterministic response (some actions trigger a fixed outcome), and unbounded reciprocal response (alternating counterplays are permitted). A less-explored but strategically rich structure is the bounded one-sided response, where a player's action briefly transfers control to the opponent, who must satisfy a fixed condition through one or more moves before the turn resolves. We term games featuring this mechanism Bounded One-Sided Response Games (BORGs). We introduce a modified version of Monopoly Deal as a benchmark environment that isolates this dynamic, where a Rent action forces the opponent to choose payment assets. The gold-standard algorithm, Counterfactual Regret Minimization (CFR), converges on effective strategies without novel algorithmic extensions. A lightweight full-stack research platform unifies the environment, a parallelized CFR runtime, and a human-playable web interface. The trained CFR agent and source code are available at https://monopolydeal.ai.

Abstract-- With academic and commercial interest for humanoid robots peaking, multiple platforms are being developed. Most of these systems remain closed-source or have high acquisition and maintenance costs, however . In this work, we present AGILOped -- an open-source humanoid robot that closes the gap between high performance and accessibility. Our robot is driven by off-the-shelf backdrivable actuators with high power density and uses standard electronic components. With a height of 110 cm and weighing only 14.5 kg, AGILOped can be operated without a gantry by a single person. Experiments in walking, jumping, impact mitigation and getting-up demonstrate its viability for use in research. Humanoid robotics is currently experiencing a surge in development, fueled by the elusive idea of universal automatons capable of performing work at a level similar to humans. This is the result of several key technologies maturing to the point of viability.

This paper presents ZeloS, a research platform designed and built for practical validation of automated driving methods in an early stage of research. We overview ZeloS' hardware setup and automation architecture and focus on motion planning and control. ZeloS weighs 69 kg, measures a length of 117 cm, and is equipped with all-wheel steering, all-wheel drive, and various onboard sensors for localization. The hardware setup and the automation architecture of ZeloS are designed and built with a focus on modularity and the goal of being simple yet effective. The modular design allows the modification of individual automation modules without the need for extensive onboarding into the automation architecture. As such, this design supports ZeloS in being a versatile research platform for validating various automated driving methods. The motion planning component and control of ZeloS feature optimization-based methods that allow for explicitly considering constraints. We demonstrate the hardware and automation setup by presenting experimental data.

This paper introduces a chain-driven, sandwich-legged, mid-size quadruped robot designed as an accessible research platform. The design prioritizes enhanced locomotion capabilities, improved reliability and safety of the actuation system, and simplified, cost-effective manufacturing processes. Locomotion performance is optimized through a sandwiched leg design and a dual-motor configuration, reducing leg inertia for agile movements. Reliability and safety are achieved by integrating robust cable strain reliefs, efficient heat sinks for motor thermal management, and mechanical limits to restrict leg motion. Simplified design considerations include a quasi-direct drive (QDD) actuator and the adoption of low-cost fabrication techniques, such as laser cutting and 3D printing, to minimize cost and ensure rapid prototyping. The robot weighs approximately 25 kg and is developed at a cost under \$8000, making it a scalable and affordable solution for robotics research. Experimental validations demonstrate the platform's capability to execute trot and crawl gaits on flat terrain and slopes, highlighting its potential as a versatile and reliable quadruped research platform.

Abstract-- We present a contact-based phenotyping robot platform that can autonomously insert nitrate sensors into cornstalks to proactively monitor macronutrient levels in crops. This task is challenging because inserting such sensors requires sub-centimeter precision in an environment which contains high levels of clutter, lighting variation, and occlusion. To address these challenges, we develop a robust perceptionaction pipeline to detect and grasp stalks, and create a custom robot gripper which mechanically aligns the sensor before inserting it into the stalk. Through experimental validation on 48 unique stalks in a cornfield in Iowa, we demonstrate our platform's capability of detecting a stalk with 94% success, grasping a stalk with 90% success, and inserting a sensor with 60% success. In addition to developing an autonomous phenotyping research platform, we share key challenges and insights obtained from deployment in the field. With the development of artificial intelligence in computer vision and robotics, the agricultural sector is poised to implement precision agriculture methods to enhance crop production efficiency and minimize environmental footprint [1]. Figure 1: Robot inserting sensors into cornstalks to monitor plant nitrate concentration in Curtiss Farm, Iowa.

This paper introduces a novel, small form-factor, aerial vehicle research platform for agile object detection, classification, tracking, and interaction tasks. General-purpose hardware components were designed to augment a given aerial vehicle and enable it to perform safe and reliable grasping. These components include a custom collision tolerant cage and low-cost Gripper Extension Package, which we call GREP, for object grasping. Small vehicles enable applications in highly constrained environments, but are often limited by computational resources. This work evaluates the challenges of pick-and-place tasks, with entirely onboard computation of object pose and visual odometry based state estimation on a small platform, and demonstrates experiments with enough accuracy to reliably grasp objects. In a total of 70 trials across challenging cases such as cluttered environments, obstructed targets, and multiple instances of the same target, we demonstrated successfully grasping the target in 93% of trials. Both the hardware component designs and software framework are released as open-source, since our intention is to enable easy reproduction and application on a wide range of small vehicles.

And with it, so are the digital labor platforms used by many AI companies to employ human gig workers. Those people perform the vital but often unseen labor of generating or labeling the masses of data that AI systems heavily rely on--often as part of efforts to make AIs more reliable and less biased. Even as these workers take on the vital task of making modern AI safer, the companies that employ them are uniformly failing to meet even a basic threshold of labor rights standards, according to a new report from the University of Oxford's Internet Institute, shared exclusively with TIME. Researchers assessed 15 digital work platforms--among them Amazon Mechanical Turk, Scale AI and Appen--and found that all of them were "still far from safeguarding basic standards of fair work," according to the report. "While the run for AI deployments gets public hype and momentum, workers behind the design, building and testing of these technological solutions, unfortunately, still face enormous challenges and experience unfair working conditions," the report says.

Radix Trading is a proprietary firm focused on quantitative research and scientific trading. We're one of the most active liquidity providers on electronic exchanges globally, and have leveraged a culture of open, collaborative innovation to scale the reach of our ideas and pace of iteration, without having to scale our headcount (currently, we're around 125 people across Chicago, New York, and Amsterdam). In our industry, the vast majority of ideas will fail. So, since inception, we've focused on continuous enhancement of our automated research platform and cutting-edge technology, allowing us to fail faster than the day prior, glean insights from each idea, and leverage individual contributions to the fullest across our entire organization. We're led by Ben Blander and Michael Rauchman, who played key roles in the rise of electronic trading, but both recognized a major gap in the industry -- a true focus on research processes coupled with an open organizational structure that fosters collaboration.

We discuss a platform that has both software and hardware components, and whose purpose is to support research into characterizing and mitigating the sim-to-real gap in robotics and vehicle autonomy engineering. The software is operating-system independent and has three main components: a simulation engine called Chrono, which supports high-fidelity vehicle and sensor simulation; an autonomy stack for algorithm design and testing; and a development environment that supports visualization and hardware-in-the-loop experimentation. The accompanying hardware platform is a 1/6th scale vehicle augmented with reconfigurable mountings for computing, sensing, and tracking. Since this vehicle platform has a digital twin within the simulation environment, one can test the same autonomy perception, state estimation, or controls algorithms, as well as the processors they run on, in both simulation and reality. A demonstration is provided to show the utilization of this platform for autonomy research. Future work will concentrate on augmenting ART/ATK with support for a full-sized Chevy Bolt EUV, which will be made available to this group in the immediate future.

By automating scientific processes and introducing artificial intelligence for decision-making, TRI's new closed-loop research platforms free up scientists' time for more creative tasks. When I first started graduate school almost 10 years ago, I was mixing ingredients by hand, writing down reaction conditions on a piece of paper, and grabbing a quick lunch in between lab sessions. At that time, the idea of a robot doing my experiments -- or using machine learning to predict the outcomes of my reactions -- would have never occurred to me. I accepted a future as a scientist where I would only be able to explore a tiny fraction of the billions of possible materials in the universe by hand. If lucky, a scientific discovery might arrive serendipitously as I became better at making "educated guesses."