Neural Information Processing Systems http://nips.cc/

Generation of dynamic, scalable multi-species bird soundscapes remains a significant challenge in computer music and algorithmic sound design. Birdsongs involve rapid frequency-modulated chirps, complex amplitude envelopes, distinctive acoustic patterns, overlapping calls, and dynamic inter-bird interactions, all of which require precise temporal and spatial control in 3D environments. Existing approaches, whether Digital Signal Processing (DSP)-based or data-driven, typically focus only on single species modeling, static call structures, or synthesis directly from recordings, and often suffer from noise, limited flexibility, or large data needs. To address these challenges, we present a novel, fully algorithm-driven framework that generates dynamic multi-species bird soundscapes using DSP-based chirp generation and 3D spatialization, without relying on recordings or training data. Our approach simulates multiple independently-moving birds per species along different moving 3D trajectories, supporting controllable chirp sequences, overlapping choruses, and realistic 3D motion in scalable soundscapes while preserving species-specific acoustic patterns. A visualization interface provides bird trajectories, spectrograms, activity timelines, and sound waves for analytical and creative purposes. Both visual and audio evaluations demonstrate the ability of the system to generate dense, immersive, and ecologically inspired soundscapes, highlighting its potential for computer music, interactive virtual environments, and computational bioacoustics research.

The world model's extracted features are fed into compact and simple policies trained by evolution, achieving state of the art results in various

This work investigates how robot-mediated physicality influences the perception of social-physical interactions with virtual characters. ETHOS (Encountered-Type Haptics for On-demand Social interaction) is an encountered-type haptic display that integrates a torque-controlled manipulator and interchangeable props with a VR headset to enable three gestures: object handovers, fist bumps, and high fives. We conducted a user study to examine how ETHOS adds physicality to virtual character interactions and how this affects presence, realism, enjoyment, and connection metrics. Each participant experienced one interaction under three conditions: no physicality (NP), static physicality (SP), and dynamic physicality (DP). SP extended the purely virtual baseline (NP) by introducing tangible props for direct contact, while DP further incorporated motion and impact forces to emulate natural touch. Results show presence increased stepwise from NP to SP to DP. Realism, enjoyment, and connection also improved with added physicality, though differences between SP and DP were not significant. Comfort remained consistent across conditions, indicating no added psychological friction. These findings demonstrate the experiential value of ETHOS and motivate the integration of encountered-type haptics into socially meaningful VR experiences.

ETHOS (Encountered-Type Haptics for On-demand Social interaction) enables corresponding virtual and physical renderings of dynamic interpersonal interactions, demonstrated here with an object handover (left), fist bump (centre), and high five (right). Abstract-- We present ETHOS (Encountered-Type Haptics for On-demand Social interaction), a dynamic encountered-type haptic display (ETHD) that enables natural physical contact in virtual reality (VR) during social interactions such as handovers, fist bumps, and high-fives. The system integrates a torque-controlled robotic manipulator with interchangeable passive props (silicone hand replicas and a baton), marker-based physical-virtual registration via a ChArUco board, and a safety monitor that gates motion based on the user's head and hand pose. We introduce two control strategies: (i) a static mode that presents a stationary prop aligned with its virtual counterpart, consistent with prior ETHD baselines, and (ii) a dynamic mode that continuously updates prop position by exponentially blending an initial mid-point trajectory with real-time hand tracking, generating a unique contact point for each interaction. Bench tests show static colocation accuracy of 5.09 0.94 mm, while user interactions achieved temporal alignment with an average contact latency of 28.58 31.21 These results demonstrate the feasibility of recreating socially meaningful haptics in VR. By incorporating essential safety and control mechanisms, ETHOS establishes a practical foundation for high-fidelity, dynamic interpersonal interactions in virtual environments. I. INTRODUCTION Virtual reality (VR) enables embodied engagement with digital environments and creates immersive experiences that unlock novel affordances. Advances in hardware and content creation over the past decade have driven increasing interest in the field, supporting the adoption of VR across a broad range of domains.

Abstract-- Semantic SLAM (Simultaneous Localization and Mapping) systems enrich robot maps with structural and semantic information, enabling robots to operate more effectively in complex environments. However, these systems struggle in real-world scenarios with occlusions, incomplete data, or ambiguous geometries, as they cannot fully leverage the higher-level spatial and semantic knowledge humans naturally apply. We introduce HICS-SLAM, a Human-in-the-Loop semantic SLAM framework that uses a shared extended reality environment for real-time collaboration. The system allows human operators to directly interact with and visualize the robot's 3D scene graph, and add high-level semantic concepts (e.g., rooms or structural entities) into the mapping process. We propose a graph-based semantic fusion methodology that integrates these human interventions with robot perception, enabling scalable collaboration for enhanced situational awareness. Experimental evaluations on real-world construction site datasets demonstrate improvements in room detection accuracy, map precision, and semantic completeness compared to automated baselines, demonstrating both the effectiveness of the approach and its potential for future extensions.

Extensive research has already been conducted in the autonomous driving field to help vehicles navigate safely and efficiently. At the same time, plenty of current research on vulnerable road user (VRU) safety is performed which largely concentrates on perception, localization, or trajectory prediction of VRUs. However, existing research still exhibits several gaps, including the lack of a unified planning and collision avoidance system for autonomous vehicles, limited investigation into delay tolerant control strategies, and the absence of an efficient and standardized testing methodology. Ensuring VRU safety remains one of the most pressing challenges in autonomous driving, particularly in dynamic and unpredictable environments. In this two year project, we focused on applying the Vehicle in Virtual Environment (VVE) method to develop, evaluate, and demonstrate safety functions for Vulnerable Road Users (VRUs) using automated steering and braking of ADS. In this current second year project report, our primary focus was on enhancing the previous year results while also considering bicyclist safety.

Predicting user intentions in virtual reality (VR) is crucial for creating im-mersive experiences, particularly in tasks involving complex grasping motions where accurate haptic feedback is essential. In this work, we leverage time-series data from hand movements to evaluate both classification and regression approaches across 810 trials with varied object types, sizes, and manipulations. Our findings reveal that classification models struggle to generalize across users, leading to inconsistent performance. In contrast, regression-based approaches, particularly those using Long Short Term Memory (LSTM) networks, demonstrate more robust performance, with timing errors within 0.25 seconds and distance errors around 5-20 cm in the critical two-second window before a grasp. Despite these improvements, predicting precise hand postures remains challenging. Through a comprehensive analysis of user variability and model interpretability, we explore why certain models fail and how regression models better accommodate the dynamic and complex nature of user behavior in VR. Our results underscore the potential of machine learning models to enhance VR interactions, particularly through adaptive haptic feedback, and lay the groundwork for future advancements in real-time prediction of user actions in VR.