We introduce SoundSpaces 2.0, a platform for on-the-fly geometry-based audio rendering for 3D environments. Given a 3D mesh of a real-world environment, SoundSpaces can generate highly realistic acoustics for arbitrary sounds captured from arbitrary microphone locations. Together with existing 3D visual assets, it supports an array of audio-visual research tasks, such as audio-visual navigation, mapping, source localization and separation, and acoustic matching. Compared to existing resources, SoundSpaces 2.0 has the advantages of allowing continuous spatial sampling, generalization to novel environments, and configurable microphone and material properties. To our knowledge, this is the first geometry-based acoustic simulation that offers high fidelity and realism while also being fast enough to use for embodied learning.

Air-ground collaborative intelligence is becoming a key approach for next-generation urban intelligent transportation management, where aerial and ground systems work together on perception, communication, and decision-making. However, the lack of a unified multi-modal simulation environment has limited progress in studying cross-domain perception, coordination under communication constraints, and joint decision optimization. To address this gap, we present TranSimHub, a unified simulation platform for air-ground collaborative intelligence. TranSimHub offers synchronized multi-view rendering across RGB, depth, and semantic segmentation modalities, ensuring consistent perception between aerial and ground viewpoints. It also supports information exchange between the two domains and includes a causal scene editor that enables controllable scenario creation and counterfactual analysis under diverse conditions such as different weather, emergency events, and dynamic obstacles. We release TranSimHub as an open-source platform that supports end-to-end research on perception, fusion, and control across realistic air and ground traffic scenes. Our code is available at https://github.com/Traffic-Alpha/TransSimHub.

The development of embodied agents for complex commercial environments is hindered by a critical gap in existing robotics datasets and benchmarks, which primarily focus on household or tabletop settings with short-horizon tasks. To address this limitation, we introduce MarketGen, a scalable simulation platform with automatic scene generation for complex supermarket environments. MarketGen features a novel agent-based Procedural Content Generation (PCG) framework. It uniquely supports multi-modal inputs (text and reference images) and integrates real-world design principles to automatically generate complete, structured, and realistic supermarkets. We also provide an extensive and diverse 3D asset library with a total of 1100+ supermarket goods and parameterized facilities assets. Building on this generative foundation, we propose a novel benchmark for assessing supermarket agents, featuring two daily tasks in a supermarket: (1) Checkout Unloading: long-horizon tabletop tasks for cashier agents, and (2) In-Aisle Item Collection: complex mobile manipulation tasks for salesperson agents. We validate our platform and benchmark through extensive experiments, including the deployment of a modular agent system and successful sim-to-real transfer. MarketGen provides a comprehensive framework to accelerate research in embodied AI for complex commercial applications.

Our method constructs an impulse response field that inherently encodes wave propagation principles and achieves state-of-the-art performance in synthesizing impulse responses for novel poses.

Infancy is characterized by rapid body growth and an explosive change of sensory and motor abilities. However, developmental robots and simulation platforms are typically designed in the image of a specific age, which limits their ability to capture the changing abilities and constraints of developing infants. To address this issue, we present MIMo v2, a new version of the multimodal infant model. It includes a growing body with increasing actuation strength covering the age range from birth to 24 months. It also features foveated vision with developing visual acuity as well as sensorimotor delays modeling finite signal transmission speeds to and from an infant's brain. Further enhancements of this MIMo version include an inverse kinematics module, a random environment generator and updated compatiblity with third-party simulation and learning libraries. Overall, this new MIMo version permits increased realism when modeling various aspects of sensorimotor development. The code is available on the official repository (https://github.com/trieschlab/MIMo).

Ensuring realistic traffic dynamics is a prerequisite for simulation platforms to evaluate the reliability of self-driving systems before deployment in the real world. Because most road users are human drivers, reproducing their diverse behaviors within simulators is vital. Existing solutions, however, typically rely on either handcrafted heuristics or narrow data-driven models, which capture only fragments of real driving behaviors and offer limited driving style diversity and interpretability. To address this gap, we introduce HDSim, an HD traffic generation framework that combines cognitive theory with large language model (LLM) assistance to produce scalable and realistic traffic scenarios within simulation platforms. The framework advances the state of the art in two ways: (i) it introduces a hierarchical driver model that represents diverse driving style traits, and (ii) it develops a Perception-Mediated Behavior Influence strategy, where LLMs guide perception to indirectly shape driver actions. Experiments reveal that embedding HDSim into simulation improves detection of safety-critical failures in self-driving systems by up to 68% and yields realism-consistent accident interpretability.

Recent advancements in embodied AI and robotic manipulation have highlighted the need for scalable, interactive simulation environments that support both scene construction and data collection for training autonomous agents. While existing platforms [1, 2, 3, 4] offer partial solutions, such as scene generation[5, 3, 4] or task-specific manipulation datasets [6, 7, 8], few provide a unified framework that integrates high-fidelity scene construction with flexible mobile robotic data collection system. To bridge this gap, we present AgentWorld, an interactive simulation platform designed for procedural scene construction and mobile-based teleoperation, enabling efficient data collection for imitation learning in complex household environments. AgentWorld addresses two critical challenges in embodied AI research: (1) stable and diverse scene generation, ensuring that the simulated environments are visually realistic and physically plausible, and (2) a comprehensive data collection system in simulation, which allows seamless control of mobile bases and robotic arms for data collection. AgentWorld is built upon NVIDIA's Omniverse Isaac Sim [9] and Unreal Engine [10], allowing it to inherit both strengths including the physics engine for robot parallel training and realistic rendering effects.