Although robots are becoming more advanced with human-like anthropomorphic features and decision-making abilities to improve collaboration, the active integration of humans into this process remains under-explored. This article presents the first experimental study exploring decision-making interactions between humans and robots with visual cues from robotic eyes, which can dynamically influence human opinion formation. The cues generated by robotic eyes gradually guide human decisions towards alignment with the robot's choices. Both human and robot decision-making processes are modeled as non-linear opinion dynamics with evolving biases. To examine these opinion dynamics under varying biases, we conduct numerical parametric and equilibrium continuation analyses using tuned parameters designed explicitly for the presented human-robot interaction experiment. Furthermore, to facilitate the transition from disagreement to agreement, we introduced a human opinion observation algorithm integrated with the formation of the robot's opinion, where the robot's behavior is controlled based on its formed opinion. The algorithms developed aim to enhance human involvement in consensus building, fostering effective collaboration between humans and robots. Experiments with 51 participants (N = 51) show that human-robot teamwork can be improved by guiding human decisions using robotic cues. Finally, we provide detailed insights on the effects of trust, cognitive load, and participant demographics on decision-making based on user feedback and post-experiment interviews.

Pre-training for Reinforcement Learning (RL) with purely video data is a valuable yet challenging problem. Although in-the-wild videos are readily available and inhere a vast amount of prior world knowledge, the absence of action annotations and the common domain gap with downstream tasks hinder utilizing videos for RL pre-training. To address the challenge of pre-training with videos, we propose Pre-trained Visual Dynamics Representations (PVDR) to bridge the domain gap between videos and downstream tasks for efficient policy learning. By adopting video prediction as a pre-training task, we use a Transformer-based Conditional Variational Autoencoder (CVAE) to learn visual dynamics representations. The pre-trained visual dynamics representations capture the visual dynamics prior knowledge in the videos. This abstract prior knowledge can be readily adapted to downstream tasks and aligned with executable actions through online adaptation. We conduct experiments on a series of robotics visual control tasks and verify that PVDR is an effective form for pre-training with videos to promote policy learning.

Abstract-- This study presents a transformer-based approach for fault-tolerant control in fixed-wing Unmanned Aerial Vehicles (UAVs), designed to adapt in real time to dynamic changes caused by structural damage or actuator failures. Employing a teacher-student knowledge distillation framework, the proposed approach trains a student agent with partial observations by transferring knowledge from a privileged expert agent with full observability, enabling robust performance across diverse failure scenarios. In recent years, Unmanned Aerial Vehicles (UAVs) have been widely used to perform various applications in complex However, complex environments and demanding tasks can and critical scenarios, such as search and rescue or cause structural damage to the UAV, altering its aerodynamic autonomous medical transportation. Fixed-wing UAVs, in particular, and reliability of these aerial robots have become major exhibit highly complex, nonlinear dynamics, which can concerns due to the potential implications of system failures. Unlike other robotics fields, such as manipulation and Although current FCSs are robust, they struggle to maintain humanoid locomotion, where advanced control methods are performance when the vehicle dynamics deviate from the essential for managing complex joint movements, UAV original design specifications, sometimes leading to control Flight Control Systems (FCSs) in industry typically rely divergence and catastrophic failure.

Reinforcement Learning (RL) agents are able to solve a wide variety of tasks but are prone to producing unsafe behaviors. Constrained Markov Decision Processes (CMDPs) provide a popular framework for incorporating safety constraints. However, common solution methods often compromise reward maximization by being overly conservative or allow unsafe behavior during training. We propose Constrained Trust Region Policy Optimization (C-TRPO), a novel approach that modifies the geometry of the policy space based on the safety constraints and yields trust regions composed exclusively of safe policies, ensuring constraint satisfaction throughout training. We theoretically study the convergence and update properties of C-TRPO and highlight connections to TRPO, Natural Policy Gradient (NPG), and Constrained Policy Optimization (CPO). Finally, we demonstrate experimentally that C-TRPO significantly reduces constraint violations while achieving competitive reward maximization compared to state-of-theart CMDP algorithms. Reinforcement Learning (RL) has emerged as a highly successful paradigm in machine learning for solving sequential decision and control problems, with policy gradient (PG) algorithms as a popular approach (Williams, 1992; Sutton et al., 1999; Konda & Tsitsiklis, 1999).

The advent of generalist Large Language Models (LLMs) and Large Vision Models (VLMs) have streamlined the construction of semantically enriched maps that can enable robots to ground high-level reasoning and planning into their representations. One of the most widely used semantic map formats is the 3D Scene Graph, which captures both metric (low-level) and semantic (high-level) information. However, these maps often assume a static world, while real environments, like homes and offices, are dynamic. Even small changes in these spaces can significantly impact task performance. To integrate robots into dynamic environments, they must detect changes and update the scene graph in real-time. This update process is inherently multimodal, requiring input from various sources, such as human agents, the robot's own perception system, time, and its actions. This work proposes a framework that leverages these multimodal inputs to maintain the consistency of scene graphs during real-time operation, presenting promising initial results and outlining a roadmap for future research.

Large Language Models (LLMs) demonstrate exceptional capabilities in various scenarios. However, they suffer from much redundant information and are sensitive to the position of key information (relevant to the input question) in long context scenarios, leading to inferior performance. To address these challenges, we present Perception Compressor, a training-free prompt compression method. It includes a perception retriever that leverages guiding questions and instruction to retrieve the most relevant demonstrations, a dual-slope ratio allocator to dynamically allocate compression ratios and open-book ratios, and a semi-guided iterative compression that retains key information at the token level while removing tokens that distract the LLM. We conduct extensive experiments on long context benchmarks, i.e., NaturalQuestions, LongBench, and MuSiQue. Experiment results show that Perception Compressor outperforms existing methods by a large margin, achieving state-of-the-art performance.

Generalization outside the scope of one's training data requires leveraging prior knowledge about the effects that transfer, and the effects that don't, between different data sources. Bayesian transfer learning is a principled paradigm for specifying this knowledge, and refining it on the basis of data from the source (training) and target (prediction) tasks. We address the challenging transfer learning setting where the learner (i) cannot fine-tune in the target task, and (ii) does not know which source data points correspond to the same task (i.e., the data sources are unknown). We propose a proxy-informed robust method for probabilistic transfer learning (PROMPT), which provides a posterior predictive estimate tailored to the structure of the target task, without requiring the learner have access to any outcome information from the target task. Instead, PROMPT relies on the availability of proxy information. PROMPT uses the same proxy information for two purposes: (i) estimation of effects specific to the target task, and (ii) construction of a robust reweighting of the source data for estimation of effects that transfer between tasks. We provide theoretical results on the effect of this reweighting on the risk of negative transfer, and demonstrate application of PROMPT in two synthetic settings.

We study the problem of globally optimising a target variable of an unknown causal graph on which a sequence of soft or hard interventions can be performed. The problem of optimising the target variable associated with a causal graph is formalised as Causal Bayesian Optimisation (CBO). We study the CBO problem under the cumulative regret objective with unknown causal graphs for two settings, namely structural causal models with hard interventions and function networks with soft interventions. We propose Graph Agnostic Causal Bayesian Optimisation (GACBO), an algorithm that actively discovers the causal structure that contributes to achieving optimal rewards. GACBO seeks to balance exploiting the actions that give the best rewards against exploring the causal structures and functions. To the best of our knowledge, our work is the first to study causal Bayesian optimization with cumulative regret objectives in scenarios where the graph is unknown or partially known. We show our proposed algorithm outperforms baselines in simulated experiments and real-world applications.

We propose reinterpreting copula density estimation as a discriminative task. Under this novel estimation scheme, we train a classifier to distinguish samples from the joint density from those of the product of independent marginals, recovering the copula density in the process. We derive equivalences between well-known copula classes and classification problems naturally arising in our interpretation. Furthermore, we show our estimator achieves theoretical guarantees akin to maximum likelihood estimation. By identifying a connection with density ratio estimation, we benefit from the rich literature and models available for such problems. Empirically, we demonstrate the applicability of our approach by estimating copulas of real and high-dimensional datasets, outperforming competing copula estimators in density evaluation as well as sampling.

Extreme events are evolving as a direct consequence of climate change, leading to the emergence of new, previously unobserved phenomena [1, 2]. In remote regions like the Arctic, where in-situ measurements are sparse, scientists must increasingly depend on analytical and numerical models to explore these events. However, modeling in such regions presents significant challenges due to the uncertainties in the data required to calibrate and validate these models [3]. Consequently, large simplifications are often necessary, resulting in substantial discrepancies between observed and modeled phenomena. The mysterious 10.88 mHz very-long-period (VLP) seismic signal, which appeared following a tsunamigenic landslide in the Dickson Fjord, Greenland, on September 16th, 2023, and the subsequent interdisciplinary scientific efforts to determine its origin, underscore these challenges. Two independent studies [4, 5] have hypothesized that the signal was driven by a standing wave, or seiche, which formed in the aftermath of the tsunami. While it is well-documented that seiches can form in resonant enclosed and semi-enclosed basins [6], the loading-induced tilt they produce has only been observed locally (< 30 km) and for short durations (< 1 hour)[5, 7]. Moreover, no prior evidence exists of persistent fluid sloshing (lasting several days) without an external driver.