Sudan's Rapid Support Forces (RSF) have launched a series of drone attacks targeting humanitarian aid convoys and fuel trucks across North Kordofan, killing at least one person and wounding several others, officials and medical organisations said. The North Kordofan state government condemned Friday's strikes on a convoy linked to the World Food Programme (WFP), urging the international community and United Nations bodies to impose sanctions on the RSF paramilitary group's leadership. The attacks occurred along the key road connecting the state capital, el-Obeid, with Kosti in neighbouring White Nile state. Fighting between the government-aligned Sudanese Armed Forces (SAF) and the RSF has intensified across the Kordofan region since October 2025 after el-Fasher fell to the RSF, where the group committed atrocities - a "crime scene" according to the UN. According to the UN Office for the Coordination of Humanitarian Affairs (OCHA), the first strike at dawn hit three trucks in Er-Rahad.

WIRED put the latest consumer exoskeletons from Dnsys and Hypershell in a head-to-head test on a pro athletic track. Personal exoskeletons were everywhere at CES 2026 . There were ambitious designs from newcomers WiRobotics, Sumbu, Ascentiz, and Dephy, while Skip Mo/Go was back promoting its long-overdue tech trousers. Dnsys (pronounced Deen-sis), a comparatively well established name, had some new launches to tease, Hypershell was back with its top model, and Ascentiz had us sprinting across the show floor . An exoskeleton is a relatively new class of wearable device designed to enhance, support, or assist human movement, strength, posture, or even physical activity.

Yann LeCun's new venture is a contrarian bet against large language models In an exclusive interview, the AI pioneer shares his plans for his new Paris-based company, AMI Labs. Yann LeCun is a Turing Award recipient and a top AI researcher, but he has long been a contrarian figure in the tech world. He believes that the industry's current obsession with large language models is wrong-headed and will ultimately fail to solve many pressing problems. Instead, he thinks we should be betting on world models--a different type of AI that accurately reflects the dynamics of the real world. He is also a staunch advocate for open-source AI and criticizes the closed approach of frontier labs like OpenAI and Anthropic. Perhaps it's no surprise, then, that he recently left Meta, where he had served as chief scientist for FAIR (Fundamental AI Research), the company's influential research lab that he founded. Meta has struggled to gain much traction with its open-source AI model Llama and has seen internal shake-ups, including the controversial acquisition of ScaleAI. LeCun sat down with in an exclusive online interview from his Paris apartment to discuss his new venture, life after Meta, the future of artificial intelligence, and why he thinks the industry is chasing the wrong ideas.

The rapid development of artificial intelligence (AI) has unearthed the potential to assist humans in controlling advanced technologies. Shared autonomy (SA) facilitates control by combining inputs from a human pilot and an AI copilot. In prior SA studies, the copilot is constantly active in determining the action played at each time step. This limits human autonomy that may have deleterious effects on performance. In general, the amount of helpful copilot assistance varies greatly depending on the task dynamics.

Assistive agents should make humans' lives easier. Classically, such assistance is studied through the lens of inverse reinforcement learning, where an assistive agent (e.g., a chatbot, a robot) infers a human's intention and then selects actions to help the human reach that goal. This approach requires inferring intentions, which can be difficult in high-dimensional settings. We build upon prior work that studies assistance through the lens of empowerment: an assistive agent aims to maximize the influence of the human's actions such that they exert a greater control over the environmental outcomes and can solve tasks in fewer steps. We lift the major limitation of prior work in this area--scalability to high-dimensional settings--with contrastive successor representations. We formally prove that these representations estimate a similar notion of empowerment to that studied by prior work and provide a ready-made mechanism for optimizing it. Empirically, our proposed method outperforms prior methods on synthetic benchmarks, and scales to Overcooked, a cooperative game setting. Theoretically, our work connects ideas from information theory, neuroscience, and reinforcement learning, and charts a path for representations to play a critical role in solving assistive problems.

Embodied AI agents continue to become more capable every year with the advent of new models, environments, and benchmarks, but are still far away from being performant and reliable enough to be deployed in real, user-facing, applications. In this paper, we ask: can we bridge this gap by enabling agents to ask for assistance from an expert such as a human being? To this end, we propose the Ask4Help policy that augments agents with the ability to request, and then use expert assistance. Ask4Help policies can be efficiently trained without modifying the original agent's parameters and learn a desirable trade-off between task performance and the amount of requested help, thereby reducing the cost of querying the expert. We evaluate Ask4Help on two different tasks -- object goal navigation and room rearrangement and see substantial improvements in performance using minimal help. On object navigation, an agent that achieves a $52\%$ success rate is raised to $86\%$ with $13\%$ help and for rearrangement, the state-of-the-art model with a $7\%$ success rate is dramatically improved to $90.4\%$ using $39\%$ help. Human trials with Ask4Help demonstrate the efficacy of our approach in practical scenarios.

One difficulty in using artificial agents for human-assistive applications lies in the challenge of accurately assisting with a person's goal(s). Existing methods tend to rely on inferring the human's goal, which is challenging when there are many potential goals or when the set of candidate goals is difficult to identify. We propose a new paradigm for assistance by instead increasing the human's ability to control their environment, and formalize this approach by augmenting reinforcement learning with human empowerment. This task-agnostic objective increases the person's autonomy and ability to achieve any eventual state. We test our approach against assistance based on goal inference, highlighting scenarios where our method overcomes failure modes stemming from goal ambiguity or misspecification. As existing methods for estimating empowerment in continuous domains are computationally hard, precluding its use in real time learned assistance, we also propose an efficient empowerment-inspired proxy metric. Using this, we are able to successfully demonstrate our method in a shared autonomy user study for a challenging simulated teleoperation task with human-in-the-loop training.

In recent years, many countries, including Japan, have rapidly aging populations, making the preservation of seniors' quality of life a significant concern. For elderly people with impaired physical abilities, support for toileting is one of the most important issues. This paper details the design, development, experimental assessment, and potential application of the gripper system, with a focus on the unique requirements and obstacles involved in aiding elderly or hemiplegic individuals in dressing and undressing trousers. The gripper we propose seeks to find the right balance between compliance and grasping forces, ensuring precise manipulation while maintaining a safe and compliant interaction with the users. The gripper's integration into a custom--built robotic manipulator system provides a comprehensive solution for assisting hemiplegic individuals in their dressing and undressing tasks. Experimental evaluations and comparisons with existing studies demonstrate the gripper's ability to successfully assist in both dressing and dressing of trousers in confined spaces with a high success rate. This research contributes to the advancement of assistive robotics, empowering elderly, and physically impaired individuals to maintain their independence and improve their quality of life.

Large Language Model (LLM) agents are emerging to transform daily life. However, existing LLM agents primarily follow a reactive paradigm, relying on explicit user instructions to initiate services, which increases both physical and cognitive workload. In this paper, we propose ProAgent, the first end-to-end proactive agent system that harnesses massive sensory contexts and LLM reasoning to deliver proactive assistance. ProAgent first employs a proactive-oriented context extraction approach with on-demand tiered perception to continuously sense the environment and derive hierarchical contexts that incorporate both sensory and persona cues. ProAgent then adopts a context-aware proactive reasoner to map these contexts to user needs and tool calls, providing proactive assistance. We implement ProAgent on Augmented Reality (AR) glasses with an edge server and extensively evaluate it on a real-world testbed, a public dataset, and through a user study. Results show that ProAgent achieves up to 33.4% higher proactive prediction accuracy, 16.8% higher tool-calling F1 score, and notable improvements in user satisfaction over state-of-the-art baselines, marking a significant step toward proactive assistants. A video demonstration of ProAgent is available at https://youtu.be/pRXZuzvrcVs.