The main idea is updating the most accurate prediction head to specialize each head in certain environments with similar dynamics, i.e., clustering environments.

We thank all reviewers for carefully reading our paper and their valuable comments. Below are our responses to the reviewers, which we will incorporate in the final draft. We will include more comprehensive results for all environments in the final draft. Thank you very much for your pointers. We will clarify these limitations in the final draft.

People frequently use speech-to-text systems to compose short texts with voice. However, current voice-based interfaces struggle to support composing more detailed, contextually complex texts, especially in scenarios where users are on the move and cannot visually track progress. Longer-form communication, such as composing structured emails or thoughtful responses, requires persistent context tracking, structured guidance, and adaptability to evolving user intentions--capabilities that conventional dictation tools and voice assistants do not support. We introduce StepWrite, a large language model-driven voice-based interaction system that augments human writing ability by enabling structured, hands-free and eyes-free composition of longer-form texts while on the move. StepWrite decomposes the writing process into manageable subtasks and sequentially guides users with contextually-aware non-visual audio prompts. StepWrite reduces cognitive load by offloading the context-tracking and adaptive planning tasks to the models. Unlike baseline methods like standard dictation features (e.g., Microsoft Word) and conversational voice assistants (e.g., ChatGPT Advanced Voice Mode), StepWrite dynamically adapts its prompts based on the evolving context and user intent, and provides coherent guidance without compromising user autonomy. An empirical evaluation with 25 participants engaging in mobile or stationary hands-occupied activities demonstrated that StepWrite significantly reduces cognitive load, improves usability and user satisfaction compared to baseline methods. Technical evaluations further confirmed StepWrite's capability in dynamic contextual prompt generation, accurate tone alignment, and effective fact checking. This work highlights the potential of structured, context-aware voice interactions in enhancing hands-free and eye-free communication in everyday multitasking scenarios.

Large language models (LLMs) have recently demonstrated the potential in acting as autonomous agents for sequential decision-making tasks. However, most existing methods either take actions greedily without planning or rely on static plans that are not adaptable to environmental feedback. Consequently, the sequential decision-making performance of LLM agents degenerates with problem complexity and plan horizons increase. We propose a closed-loop approach, AdaPlanner, which allows the LLM agent to refine its self-generated plan adaptively in response to environmental feedback. To mitigate hallucination, we develop a code-style LLM prompt structure that facilitates plan generation across a variety of tasks, environments, and agent capabilities. Furthermore, we propose a skill discovery mechanism that leverages successful plans as few-shot exemplars, enabling the agent to plan and refine with fewer task demonstrations.

Robots need robust and flexible vision systems to perceive and reason about their environments beyond geometry. Most of such systems build upon deep learning approaches. As autonomous robots are commonly deployed in initially unknown environments, pre-training on static datasets cannot always capture the variety of domains and limits the robot's vision performance during missions. Recently, self-supervised as well as fully supervised active learning methods emerged to improve robotic vision. These approaches rely on large in-domain pre-training datasets or require substantial human labelling effort. To address these issues, we present a recent adaptive planning framework for efficient training data collection to substantially reduce human labelling requirements in semantic terrain monitoring missions. To this end, we combine high-quality human labels with automatically generated pseudo labels. Experimental results show that the framework reaches segmentation performance close to fully supervised approaches with drastically reduced human labelling effort while outperforming purely self-supervised approaches. We discuss the advantages and limitations of current methods and outline valuable future research avenues towards more robust and flexible robotic vision systems in unknown environments.

INTRODUCTION Adaptive planning is ideally suited for the deep uncertainties presented by climate change. While there is a robust scholarship on the theory and methods of adaptive planning, this has largely neglected how adaptive planning is affected by existing planning institutions and how to move forward within the constraints of traditional planning organizations. This study asks: How do existing traditional planning institutions support adaptive planning? We explore this for flood planning in the Lower Rio Grande Valley of Texas, United States. We draw on county hazard plan and regional flood plan documents as well as transcripts of regional flood planning meetings to explore the emergent topics of these institutional outputs. Using Natural Language Processing to analyze this large amount of text, we find that hazard plans and discussions developing these plans are largely lacking an adaptive approach. KEYWORDS adaptive planning; uncertainty; flood plan; Rio Grande Valley INTRODUCTION Planning for natural hazard risk reduction in the context climate change involves decision making under conditions of interacting, multiple uncertainties. Some of these are "deep uncertainties" connected to long time horizons, nonlinear changes in climates and ecosystems, and inability to reliably quantify the rate and magnitude of climate changes (Babovic & Mijic, 2018; Bosomworth & Gaillard, 2019). Other uncertainties are associated with the ambiguities and unpredictability of socioeconomic systems, including population growth, land use change, social conflict, and the whims of political will (Babovic & Mijic 2019; Buurman & Babovic, 2014). In the face of these uncertainties, a new paradigm of decision making has emerged that emphasizes the development of adaptive plans and policies (Hassnoot et al., 2013; Walker et al., 2013). Traditional planning approaches typically generate a static optimal plan to reduce vulnerability to a single'most likely' future or to respond a wide range of plausible future scenarios (Haasnoot et al., 2013; Manocha & Babovic, 2018). Because the future is largely unknowable, static optimal plans are likely to fail and adaptations are made adhoc to adjust to emerging risk conditions (Haasnoot et al., 2013).

Whilst assistive robots [7] have been embedded into social and health care environments [1, 2, 10], they have largely been limited to simple applications, such as support for social and physical activities and hall monitoring, but often without considering potential interactions with humans. To expand the range of these applications, the human user and the robot need to interact in order to perform tasks together [4]. As such, this interaction, which is still underexplored in the social care domain, should be prioritised, with an emphasis on the safety of the human [3, 9]. To enable the development of applications that support such interaction and to ensure its safety, we propose an adaptive mission and path finding framework for an autonomous robot operating in a homecare environment. The framework models the environment as a graph, with nodes representing key locations within the environment where the robot can perform local tasks. Missions are modelled as a repertoire of locations within the environment where a task requires completion. The main contributions of our'research preview' paper are: (i) a generalised approach for modelling environments as graphs with edges represented as levels of risk, (ii) a modified Dijkstra's algorithm for performing path finding in uncertain environments with a cost function to reduce risk, (iii) simple human predictive behaviour model that forecasts human intention allowing for adaptive path finding using heat maps to artificially increase the risk associated with specific edges in the graph, (iv) a framework that combines modelling methods, adaptive path finding techniques and run-time probabilistic model generation for safety verification into an end-to-end solution for autonomous robotic mission planning, (v) finally, a simulation-based case study that shows the effectiveness of the framework.

The computational costs of inference and planning have confined Bayesian model-based reinforcement learning to one of two dismal fates: powerful Bayes-adaptive planning but only for simplistic models, or powerful, Bayesian non-parametric models but using simple, myopic planning strategies such as Thompson sampling. We ask whether it is feasible and truly beneficial to combine rich probabilistic models with a closer approximation to fully Bayesian planning. First, we use a collection of counterexamples to show formal problems with the over-optimism inherent in Thompson sampling. Then we leverage state-of-the-art techniques in efficient Bayes-adaptive planning and non-parametric Bayesian methods to perform qualitatively better than both existing conventional algorithms and Thompson sampling on two contextual bandit-like problems.