The number of planning applications for new homes in England is at its highest level for four years, new data shared with BBC Verify suggests. Applications for 335,000 homes outside London were lodged in 2025, up by 60% on 2024, according to Planning Portal, the service people use to request permission. But there are warnings that more needs to be done to meet Labour's target of building 1.5 million homes by 2029, as separate government data released on Thursday suggests there has been a decrease in house building. The Ministry of Housing, Communities and Local Government said it had overhauled the planning system and removed long-standing barriers that have held back housebuilding. The increase in planning applications for new homes in England follows controversial reforms introduced by Labour, which allow development on some lower-quality green belt land, known as grey belt .

Nature is not a blocker to housing growth and the government risks missing both its housing and nature targets if it views it as one, a cross-party group of MPs has warned in a new report. The Planning and Infrastructure Bill overrides existing habitat protections, which the government has suggested is a barrier to its target to build 1.5 million houses by the end of this parliament. But in a report published on Sunday, the Environmental Audit Committee (EAC) found the measures outlined in the bill are not enough to allow the government to meet its goals. Using nature as a scapegoat means that the government will be less effective at tackling some of the genuine challenges facing the planning system, the report said. A Ministry of Housing spokesperson said it was fixing a failing system with landmark reforms, which would deliver a win-win for the economy and the environment.

One leading planning lawyer warned such AI services could'supercharge nimbyism'. One leading planning lawyer warned such AI services could'supercharge nimbyism'. Tools that help people scan applications and find grounds for objection have potential to hit government's housebuilding plans The government's plan to use artificial intelligence to accelerate planning for new homes may be about to hit an unexpected roadblock: AI-powered nimbyism. A new service called Objector is offering "policy-backed objections in minutes" to people who are upset about planning applications near their homes. It uses generative AI to scan planning applications and check for grounds for objection, ranking these as "high", "medium" or "low" impact. It then automatically creates objection letters, AI-written speeches to deliver to the planning committees, and even AI-generated videos to "influence councillors".

Abstract--A new AUV mission planning and execution software has been tested on AUV Sentry. Dubbed DINOS-R, it draws inspiration from cognitive architectures and AUV control systems to replace the legacy MC architecture. Unlike these existing architectures, however, DINOS-R is built from the ground-up to unify symbolic decision making (for understandable, repeatable, provable behavior) with machine learning techniques and reactive behaviors, for field-readiness across oceanographic platforms. Implemented primarily in Python3, DINOS-R is extensible, modular, and reusable, with an emphasis on non-expert use as well as growth for future research in oceanography and robot algorithms. Mission specification is flexible, and can be specified declaratively. Behavior specification is similarly flexible, supporting simultaneous use of real-time task planning and hard-coded user specified plans. These features were demonstrated in the field on Sentry, in addition to a variety of simulated cases. These results are discussed, and future work is outlined. In particular, although the MC (Mission Controller) system in use on AUV Sentry has repeatedly proven itself for lawnmower patterns, it presents several key limitations stemming from its rigid implementation. Most notably, it is capable of executing basic "go-to" commands and similar functionality, but was not engineered for scalability to new mission modalities or real-time interventions.

-- Panoramic RGB-D cameras are known for their ability to produce high-quality 3D scene reconstructions. However, operating these cameras involves manually selecting viewpoints and physically transporting the camera, making the generation of a 3D model time-consuming and tedious. Additionally, the process can be challenging for novice users due to spatial constraints, such as ensuring sufficient feature overlap between viewpoint frames. T o address these challenges, we propose a fully autonomous scan planning system that generates an efficient tour plan for environment scanning, ensuring collision-free navigation and adequate overlap between viewpoints within the plan. Extensive experiments conducted in both synthetic and real-world environments validate our planner's performance against state-of-the-art view planners. In particular, our method achieved an average scan coverage of 99% in the real-world experiment, with our approach being up to 3 faster than state-of-the-art planners in total scan time. The increasing advancements in mobile robotics research have enhanced robots' ability to improve the efficiency and completeness of outcomes in various active trajectory planning tasks.

In over sixty years since its inception, the field of planning has made significant contributions to both the theory and practice of building planning software that can solve a never-before-seen planning problem. This was done through established practices of rigorous design and evaluation of planning systems. It is our position that this rigor should be applied to the current trend of work on planning with large language models. One way to do so is by correctly incorporating the insights, tools, and data from the automated planning community into the design and evaluation of LLM-based planners. The experience and expertise of the planning community are not just important from a historical perspective; the lessons learned could play a crucial role in accelerating the development of LLM-based planners. This position is particularly important in light of the abundance of recent works that replicate and propagate the same pitfalls that the planning community has encountered and learned from. We believe that avoiding such known pitfalls will contribute greatly to the progress in building LLM-based planners and to planning in general.

Robots need task planning methods to achieve goals that require more than individual actions. Recently, large language models (LLMs) have demonstrated impressive performance in task planning. LLMs can generate a step-by-step solution using a description of actions and the goal. Despite the successes in LLM-based task planning, there is limited research studying the security aspects of those systems. In this paper, we develop Robo-Troj, the first multi-trigger backdoor attack for LLM-based task planners, which is the main contribution of this work. As a multi-trigger attack, Robo-Troj is trained to accommodate the diversity of robot application domains. For instance, one can use unique trigger words, e.g., "herical", to activate a specific malicious behavior, e.g., cutting hand on a kitchen robot. In addition, we develop an optimization method for selecting the trigger words that are most effective. Through demonstrating the vulnerability of LLM-based planners, we aim to promote the development of secured robot systems.

Radiotherapy treatment planning is a complex and time-intensive process, often impacted by inter-planner variability and subjective decision-making. To address these challenges, we introduce Dose Optimization Language Agent (DOLA), an autonomous large language model (LLM)-based agent designed for optimizing radiotherapy treatment plans while rigorously protecting patient privacy. DOLA integrates the LLaMa3.1 LLM directly with a commercial treatment planning system, utilizing chain-of-thought prompting, retrieval-augmented generation (RAG), and reinforcement learning (RL). Operating entirely within secure local infrastructure, this agent eliminates external data sharing. We evaluated DOLA using a retrospective cohort of 18 prostate cancer patients prescribed 60 Gy in 20 fractions, comparing model sizes (8 billion vs. 70 billion parameters) and optimization strategies (No-RAG, RAG, and RAG+RL) over 10 planning iterations. The 70B model demonstrated significantly improved performance, achieving approximately 16.4% higher final scores than the 8B model. The RAG approach outperformed the No-RAG baseline by 19.8%, and incorporating RL accelerated convergence, highlighting the synergy of retrieval-based memory and reinforcement learning. Optimal temperature hyperparameter analysis identified 0.4 as providing the best balance between exploration and exploitation. This proof of concept study represents the first successful deployment of locally hosted LLM agents for autonomous optimization of treatment plans within a commercial radiotherapy planning system. By extending human-machine interaction through interpretable natural language reasoning, DOLA offers a scalable and privacy-conscious framework, with significant potential for clinical implementation and workflow improvement.

The planning ability of Large Language Models (LLMs) has garnered increasing attention in recent years due to their remarkable capacity for multi-step reasoning and their ability to generalize across a wide range of domains. While some researchers emphasize the potential of LLMs to perform complex planning tasks, others highlight significant limitations in their performance, particularly when these models are tasked with handling the intricacies of long-horizon reasoning. In this survey, we critically investigate existing research on the use of LLMs in automated planning, examining both their successes and shortcomings in detail. We illustrate that although LLMs are not well-suited to serve as standalone planners because of these limitations, they nonetheless present an enormous opportunity to enhance planning applications when combined with other approaches. Thus, we advocate for a balanced methodology that leverages the inherent flexibility and generalized knowledge of LLMs alongside the rigor and cost-effectiveness of traditional planning methods.

Planning inspectors recommended against a Hitachi-built nuclear power plant in Anglesey on the basis that it could dilute the island's Welsh language and culture, it has emerged. Hitachi scrapped plans to build a 20bn nuclear power plant at Wylfa in 2020 over cost concerns after failing to reach a funding agreement with UK ministers. Keir Starmer's government has vowed to make it easier to build major infrastructure projects by reforming the planning system and stopping campaigners from launching "excessive" legal challenges. The prime minister unveiled plans for a historic expansion in nuclear power this week, vowing to "push past nimbyism" and make sites across the country available for new power stations. Nuclear industry figures believe that the fate of Hitachi's proposed plant at Wylfa demonstrates the problems with the UK's planning system.