West Midlands Police has admitted it overstated the evidence used to make the decision to ban Israeli fans from a match in Birmingham. Craig Guildford, its former chief constable, retired earlier this month after damning criticism of the ban on Maccabi Tel Aviv fans from the Europa League match against Aston Villa, last November. In newly released documents, the force also said we did not engage early enough with the local Jewish community, and indicated there was now a ban on AI use after its evidence included a match that did not take place. Furthermore, it said its operations would have lasted four days, involved multiple forces, and cost more than £5m, if 2,500 away fans had attended. The documents were released ahead of a public meeting on Tuesday, at which Police and Crime Commissioner for the West Midlands, Simon Foster, will discuss at his accountability and governance board, the decision to ban the Maccabi fans.

When a police force is supposed to seek the truth and uphold the law, what happens when the evidence they present to officials and the public is, as Home Secretary Shabana Mahmood put it, exaggerated or untrue? The police inspectorate has concluded the leaders of West Midlands Police fell foul of confirmation bias. In simple terms, that means senior officers had already reached a decision and were looking for intelligence to justify it. The list of errors and inaccuracies set out in an independent review of the decision-making that led to fans of Israeli football club Maccabi Tel Aviv being banned from attending a fixture at Villa Park in November have been described by Mahmood as damning. They include: A report of a football match in an intelligence report produced using AI which never happened; a twice-repeated denial by senior police leaders to MPs that AI had not been relied on to produce the inaccurate report; the claim that local Jewish groups had been consulted on the move when they had not been; inaccurately presenting evidence from Dutch police reports from a previous fixture involving the club.

Probabilistic Virtual Fixtures (VFs) enable the adaptive selection of the most suitable haptic feedback for each phase of a task, based on learned or perceived uncertainty. While keeping the human in the loop remains essential, for instance, to ensure high precision, partial automation of certain task phases is critical for productivity. We present a unified framework for probabilistic VFs that seamlessly switches between manual fixtures, semi-automated fixtures (with the human handling precise tasks), and full autonomy. We introduce a novel probabilistic Dynamical System-based VF for coarse guidance, enabling the robot to autonomously complete certain task phases while keeping the human operator in the loop. For tasks requiring precise guidance, we extend probabilistic position-based trajectory fixtures with automation allowing for seamless human interaction as well as geometry-awareness and optimal impedance gains. For manual tasks requiring very precise guidance, we also extend visual servoing fixtures with the same geometry-awareness and impedance behavior. We validate our approach experimentally on different robots, showcasing multiple operation modes and the ease of programming fixtures.

Virtual fixtures (VFs) improve precision in teleoperation but often ``fight'' the user, inflating mental workload and eroding the sense of agency. We propose Soft-Nash Virtual Fixtures, a game-theoretic shared-control policy that softens the classic two-player linear-quadratic (LQ) Nash solution by inflating the fixture's effort weight with a single, interpretable scalar parameter $τ$. This yields a continuous dial on controller assertiveness: $τ=0$ recovers a hard, performance-focused Nash / virtual fixture controller, while larger $τ$ reduce gains and pushback, yet preserve the equilibrium structure and continuity of closed-loop stability. We derive Soft-Nash from both a KL-regularized trust-region and a maximum-entropy viewpoint, obtaining a closed-form robot best response that shrinks authority and aligns the fixture with the operator's input as $τ$ grows. We implement Soft-Nash on a 6-DoF haptic device in 3D tracking task ($n=12$). Moderate softness ($τ\approx 1-3$, especially $τ=2$) maintains tracking error statistically indistinguishable from a tuned classic VF while sharply reducing controller-user conflict, lowering NASA-TLX workload, and increasing Sense of Agency (SoAS). A composite BalancedScore that combines normalized accuracy and non-fighting behavior peaks near $τ=2-3$. These results show that a one-parameter Soft-Nash policy can preserve accuracy while improving comfort and perceived agency, providing a practical and interpretable pathway to personalized shared control in haptics and teleoperation.

This study addresses a flexible holding tool for robotic disassembly. We propose a shell-type soft jig that securely and universally holds objects, mitigating the risk of component damage and adapting to diverse shapes while enabling soft fixation that is robust to recognition, planning, and control errors. The balloon-based holding mechanism ensures proper alignment and stable holding performance, thereby reducing the need for dedicated jig design, highly accurate perception, precise grasping, and finely tuned trajectory planning that are typically required with conventional fixtures. Our experimental results demonstrate the practical feasibility of the proposed jig through performance comparisons with a vise and a jamming-gripper-inspired soft jig. Tests on ten different objects further showed representative successes and failures, clarifying the jig's limitations and outlook.

The automation of robotic tasks requires high precision and adaptability, particularly in force-based operations such as insertions. Traditional learning-based approaches either rely on static datasets, which limit their ability to generalize, or require frequent manual intervention to maintain good performances. As a result, ensuring long-term reliability without human supervision remains a significant challenge. To address this, we propose an adaptive self-supervised learning framework for insertion classification that continuously improves its precision over time. The framework operates in real-time, incrementally refining its classification decisions by integrating newly acquired force data. Unlike conventional methods, it does not rely on pre-collected datasets but instead evolves dynamically with each task execution. Through real-world experiments, we demonstrate how the system progressively reduces execution time while maintaining near-perfect precision as more samples are processed. This adaptability ensures long-term reliability in force-based robotic tasks while minimizing the need for manual intervention.

The handling robot on the left picks one DLO from a bin full of DLO instances and hands it to one of the mounting robots on the right. The two mounting robots then collaboratively mount the DLO onto designated fixtures. The DLO's status is monitored by RGB-D cameras, F/T and ViTac sensors throughout the process. Abstract -- Industrial assembly of deformable linear objects (DLOs) such as cables offers great potential for many industries. However, DLOs pose several challenges for robot-based automation due to the inherent complexity of deformation and, consequentially, the difficulties in anticipating the behavior of DLOs in dynamic situations. Although existing studies have addressed isolated subproblems like shape tracking, grasping, and shape control, there has been limited exploration of integrated workflows that combine these individual processes. T o address this gap, we propose an object-centric perception and planning framework to achieve a comprehensive DLO assembly process throughout the industrial value chain. The framework utilizes visual and tactile information to track the DLO's shape as well as contact state across different stages, which facilitates effective planning of robot actions. Our approach encompasses robot-based bin picking of DLOs from cluttered environments, followed by a coordinated handover to two additional robots that mount the DLOs onto designated fixtures. Real-world experiments employing a setup with multiple robots demonstrate the effectiveness of the approach and its relevance to industrial scenarios.

Four students killed in a car crash were honoured at a university as basketball matches resumed for the first time since the incident. Makyle Bayley, 22, Eva Darold-Tchikaya, 21, Anthony "TJ" Hibbert, 24 and Daljang Wol, 22, died when a car crashed into a building on Magdalen Street, Colchester on 1 February. Mr Hibbert and Mr Wol played for the Essex Rebels, who dedicated Saturday's fixtures to the victims and held an applause in their memory. University of Essex director of sport Dave Parry said: "We've lost four really loved members of our university and sporting community, who gave so much to their friends and others." Mr Bayley was a member of the British Universities and Colleges Sport (BUCS) basketball team, while Ms Darold-Tchikaya was a member of the Essex Blades dance club and other societies.Dawid Wojtowicz/BBCSaturday's basketball fixtures at the University of Essex were dedicated to the victimsDawid Wojtowicz/BBCIt was the first time matches had been played there since the incident Last week, more than 1,000 people including students, staff and relatives of the victims attended a gathering.

--Foundation models are rapidly improving the capability of robots in performing everyday tasks autonomously such as meal preparation, yet robots will still need to be instructed by humans due to model performance, the difficulty of capturing user preferences, and the need for user agency. Robots can be instructed using various methods--natural language conveys immediate instructions but can be abstract or ambiguous, whereas end-user programming supports longer-horizon tasks but interfaces face difficulties in capturing user intent. In this work, we propose using direct manipulation of images as an alternative paradigm to instruct robots, and introduce a specific instantiation called ImageInThat which allows users to perform direct manipulation on images in a timeline-style interface to generate robot instructions. Through a user study, we demonstrate the efficacy of ImageInThat to instruct robots in kitchen manipulation tasks, comparing it to a text-based natural language instruction method. The results show that participants were faster with ImageInThat and preferred to use it over the text-based method. Supplementary material including code can be found at: https://image-in-that.github.io/. Advances in foundation models are rapidly improving the capabilities of autonomous robots, bringing us closer to robots entering our homes where they can complete everyday tasks. However, the need for human instructions will persist-- whether due to limitations in robot policies, models trained on internet-scale data that may not capture the specifics of users' environments or preferences, or simply the desire for users to maintain control over their robots' actions. For instance, a robot asked to wash dishes might follow a standard cleaning routine--e.g., by placing everything in the dishwasher and then putting them away in the cupboard--but may not respect a user's preferences-- e.g., needing to wash delicate glasses "by hand" or organizing cleaned dishes in a specific way--thus necessitating human intervention. We introduce a new paradigm for instructing robots through the direct manipulation of images. ImageInThat is a specific instantiation of this paradigm where users can manipulate images in a timeline-style interface to create instructions for the robot to execute. Existing methods for instructing robots range from those that focus on commanding the robot for the purpose of immediate execution ( e.g., uttering a language instruction to wash glasses by hand [1]) to methods that program the robot such as learning from demonstration [2] or end-user robot programming [3]. However, prior methods, whether they are used for commanding or programming, have notable drawbacks.

Long-horizon contact-rich manipulation has long been a challenging problem, as it requires reasoning over both discrete contact modes and continuous object motion. We introduce Implicit Contact Diffuser (ICD), a diffusion-based model that generates a sequence of neural descriptors that specify a series of contact relationships between the object and the environment. This sequence is then used as guidance for an MPC method to accomplish a given task. The key advantage of this approach is that the latent descriptors provide more task-relevant guidance to MPC, helping to avoid local minima for contact-rich manipulation tasks. Our experiments demonstrate that ICD outperforms baselines on complex, long-horizon, contact-rich manipulation tasks, such as cable routing and notebook folding. Additionally, our experiments also indicate that \methodshort can generalize a target contact relationship to a different environment. More visualizations can be found on our website $\href{https://implicit-contact-diffuser.github.io/}{https://implicit-contact-diffuser.github.io}$