AgentWorld: An Interactive Simulation Platform for Scene Construction and Mobile Robotic Manipulation

Abstract: We introduce AgentWorld, an interactive simulation platform for developing household mobile manipulation capabilities. Our platform combines automated scene construction that encompasses layout generation, semantic asset placement, visual material configuration, and physics simulation, with a dual-mode teleoperation system supporting both wheeled bases and humanoid locomotion policies for data collection. The resulting AgentWorld Dataset captures diverse tasks ranging from primitive actions (pick-and-place, push-pull, etc.) to multistage activities (serve drinks, heat up food, etc.) across living rooms, bedrooms, and kitchens. Through extensive benchmarking of imitation learning methods including behavior cloning, action chunking transformers, diffusion policies, and vision-language-action models, we demonstrate the dataset's effectiveness for sim-to-real transfer. The integrated system provides a comprehensive solution for scalable robotic skill acquisition in complex home environments, bridging the gap between simulation-based training and real-world deployment. The code, datasets will be available at https://yizhengzhang1.github.io/agent_world/

• The paper introduces AgentWorld, a simulation platform that enables interactive scene construction and mobile robotic manipulation.
• It employs diverse training protocols, VR-based teleoperation, and deep learning techniques to enhance robotic control through imitation learning.
• The platform validates sim-to-real transfer with extensive pick-and-place and locomotion experiments, demonstrating robust performance.

AgentWorld: An Interactive Simulation Platform for Scene Construction and Mobile Robotic Manipulation

Introduction

The paper "AgentWorld: An Interactive Simulation Platform for Scene Construction and Mobile Robotic Manipulation" introduces a sophisticated simulation environment designed to advance research in robotic manipulation and scene construction. This platform is distinguished by its capacity to conduct highly interactive, physics-based simulations that aim to bridge the gap between virtual setups and real-world applications. With AgentWorld, researchers are provided a versatile tool to simulate complex scenarios involving both mobile robotics and detailed scene configurations.

Core Capabilities and Demonstrations

AgentWorld's capabilities are vividly showcased via a supplementary video, highlighting its primary functionalities:

• Scene Construction: This section demonstrates the platform's ability to generate room layouts, perform semantic asset selection and placement, and configure visual materials. Interactive physics simulations allow for dynamic interactions between objects, essential for realistic scene construction.
• Teleoperation System: The platform supports VR-based arm and gripper teleoperation and dexterous hand control, enabling intricate manipulation of articulated assets. This feature is crucial for developing teleoperation strategies and evaluating their efficacy in simulated environments.
• Learning Results: AgentWorld facilitates imitation learning for multistage tasks and showcases successful sim-to-real transfer in pick-and-place operations. This indicates the platform’s potential in advancing algorithms capable of adapting from virtual simulations to physical tasks robustly.

Training Protocols

The paper details training methodologies for humanoid robot locomotion and manipulation tasks. By integrating diverse control commands and considering upper limb positioning, AgentWorld supports robust locomotion policy development. The platform incorporates a foot trajectory generator to optimize training efficiency, ensuring that policy networks receive adequately constrained inputs for effective learning.

Furthermore, various imitation learning algorithms are implemented within the platform. The observation space incorporates RGB inputs and proprioceptive states, effortlessly adapting to various robotic prototypes including the Unitree G1, H1, and the Franka Emika Panda arm. Notably, the implementation employs ResNet-18 for visual feature extraction and merges these with proprioceptive data through MLPs, thus enabling refined action predictions. The architecture underscores the integration of sophisticated deep learning models to enhance robotic control capabilities.

Dataset Composition

AgentWorld supports diverse datasets to facilitate comprehensive training regimes. Each dataset features numerous tasks, categorized into activities like “Pick and Place,” “Open and Close,” among others, emphasizing generalized interaction within simulated environments. The extensive number of recorded sequences per task validates the platform's commitment to providing rich data for training and benchmarking robotic algorithms.

Implications and Future Directions

The implications of AgentWorld are multifaceted. Practically, it paves the way for developing facile algorithms that can transition from simulated environments to real-world applications seamlessly. Theoretically, it opens avenues for exploring new approaches in imitation learning, sim-to-real policy transfer, and robust scene reconstruction algorithms.

Future developments in AI could see AgentWorld evolving into a critical resource for real-world testbeds, particularly as it integrates more advanced sensory and physics engines. Long-term, platforms like AgentWorld could become indispensable for developing AI-driven solutions that demand high levels of interaction and adaptability, particularly as robotic applications expand into more complex and nuanced human environments.

Conclusion

AgentWorld emerges as a comprehensive simulation framework poised to transform how researchers develop and test robotic manipulation and scene construction algorithms. Its robust design and array of features underscore its potential to accelerate advancements in both theoretical research and practical implementations. As AI and robotics research continue to evolve, platforms like AgentWorld will undoubtedly play a pivotal role in fostering innovations that drive the next generation of intelligent systems.