RoboTHOR: An Open Simulation-to-Real Embodied AI Platform (2004.06799v1)

Abstract: Visual recognition ecosystems (e.g. ImageNet, Pascal, COCO) have undeniably played a prevailing role in the evolution of modern computer vision. We argue that interactive and embodied visual AI has reached a stage of development similar to visual recognition prior to the advent of these ecosystems. Recently, various synthetic environments have been introduced to facilitate research in embodied AI. Notwithstanding this progress, the crucial question of how well models trained in simulation generalize to reality has remained largely unanswered. The creation of a comparable ecosystem for simulation-to-real embodied AI presents many challenges: (1) the inherently interactive nature of the problem, (2) the need for tight alignments between real and simulated worlds, (3) the difficulty of replicating physical conditions for repeatable experiments, (4) and the associated cost. In this paper, we introduce RoboTHOR to democratize research in interactive and embodied visual AI. RoboTHOR offers a framework of simulated environments paired with physical counterparts to systematically explore and overcome the challenges of simulation-to-real transfer, and a platform where researchers across the globe can remotely test their embodied models in the physical world. As a first benchmark, our experiments show there exists a significant gap between the performance of models trained in simulation when they are tested in both simulations and their carefully constructed physical analogs. We hope that RoboTHOR will spur the next stage of evolution in embodied computer vision. RoboTHOR can be accessed at the following link: https://ai2thor.allenai.org/robothor

RoboTHOR: An Overview of Simulation-to-Real Embodied AI Platform

The paper "RoboTHOR: An Open Simulation-to-Real Embodied AI Platform" introduces a framework for developing and evaluating embodied AI models, bridging the gap between simulation and real-world applications. The authors highlight the current challenges of generalizing models trained in simulated environments to real-world scenarios and propose RoboTHOR as a solution to facilitate more robust research in this domain.

Key Features and Components

RoboTHOR is designed to overcome several notable challenges in simulation-to-real transfer for embodied AI, emphasizing the need for tight alignment between simulated and real worlds and cost-effective experimentation. The platform comprises:

• Simulation and Real Counterparts: The framework includes both simulated training environments and corresponding physical environments. This dual setup allows systematic exploration of simulation-to-real transfer challenges.
• Modular Design: RoboTHOR scenes are constructed using a modular approach, enabling easy expansion and customization. This design flexibility supports diverse research needs and promotes scalability.
• Re-configurability: The physical environments are built with modular and movable components, allowing rapid reconfiguration to host various scenes, thereby optimizing resources and space.
• Open Access: The platform, its algorithms, and assets are open source. Researchers worldwide can remotely deploy their models on RoboTHOR's hardware at no cost, democratizing access to necessary infrastructure for embodied AI research.
• Replicability: The platform's design is easily replicable by other researchers, facilitated by open-sourced plans and readily available, low-cost materials, making it accessible for a broader research community.
• Benchmarking: It provides standardized challenges, focusing on tasks transferable between simulation and real environments, such as semantic navigation.

Experimental Benchmarks and Findings

The paper primarily benchmarks models on semantic navigation, which involves navigating towards an instance of a specified category in complex environments. Noteworthy findings from the experiments include:

1. Sim-to-Real Performance Gap: A significant decrease in performance is observed when transitioning models trained in simulation to real-world testing, indicating the complexities of real-world dynamics that simulations cannot fully capture.
2. Feature Space Disparities: The paper reveals differences in the feature space between real and simulated images, even when they appear similar visually. This discrepancy significantly impacts the models' ability to generalize.
3. Control Dynamics Variability: Real-world control dynamics vary considerably due to factors like motor noise and slippage, challenging the models' assumptions based on simulation training.
4. Domain Adaptation Challenges: Off-the-shelf image translation methods to bridge simulation and real-world appearance disparities show minimal impact on performance improvements, suggesting the need for specialized domain adaptation techniques.

Implications and Future Directions

RoboTHOR represents a significant step toward addressing the challenges inherent in simulation-to-real transfer in embodied AI. By providing an open, accessible platform, it encourages wider participation and collaboration in tackling these challenges. The comprehensive environment facilitates robust testing and development of models that must generalize across different domains, bringing researchers closer to overcoming the limitations of current simulation-based training approaches.

Future research could explore novel representation learning and domain adaptation techniques to mitigate the identified disparities in feature spaces. Additionally, enhancing the fidelity of simulations to more accurately reflect real-world dynamics could further bridge the performance gap. Ultimately, developments influenced by platforms like RoboTHOR will refine embodied AI systems, improving their applicability and effectiveness in real-world scenarios.