MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments (1712.03931v1)

Abstract: We present MINOS, a simulator designed to support the development of multisensory models for goal-directed navigation in complex indoor environments. The simulator leverages large datasets of complex 3D environments and supports flexible configuration of multimodal sensor suites. We use MINOS to benchmark deep-learning-based navigation methods, to analyze the influence of environmental complexity on navigation performance, and to carry out a controlled study of multimodality in sensorimotor learning. The experiments show that current deep reinforcement learning approaches fail in large realistic environments. The experiments also indicate that multimodality is beneficial in learning to navigate cluttered scenes. MINOS is released open-source to the research community at http://minosworld.org . A video that shows MINOS can be found at https://youtu.be/c0mL9K64q84

• The paper introduces MINOS, a versatile simulator that integrates multisensor data for indoor navigation research.
• It employs extensive datasets and procedural environment variations to benchmark reinforcement learning algorithms across defined tasks.
• Empirical findings highlight the benefits of multimodal sensor input in overcoming challenges in complex, cluttered indoor settings.

Overview of MINOS: A Multimodal Indoor Simulator for Navigation Research

The paper presents MINOS, a sophisticated and versatile simulation framework designed for research in sensorimotor control and multisensory navigation within indoor environments. Compounded by logistical challenges in using physical agents for experiments, the simulation of navigation tasks offers significant advantages in terms of scalability, safety, and flexibility. This paper's contribution is characterized by addressing these challenges through the development and benchmarking of multisensory models in complex environments.

MINOS leverages extensive datasets, namely SUNCG and Matterport3D, incorporating an extensive variety of settings with over 45,000 3D models and real-world indoor scenes, respectively. These datasets enable researchers to test navigation algorithms across a broad-spectrum of environments, enhancing the generalization potential of developed models. The simulator also supports flexible sensor suite configurations, accommodating vision, depth, surface normals, contact forces, and semantic segmentation modalities.

Core Contributions

1. Simulation Capabilities: MINOS is engineered to provide high-fidelity simulations of indoor environments, operating at high frame-rates to facilitate data-intensive training regimes typical of deep reinforcement learning algorithms. The simulation accommodates environmental variations through procedural modifications, which is essential for testing generalization efficacy of navigation models.
2. Benchmarking Framework: By defining distinct navigation tasks (PointGoal, ObjectGoal, and RoomGoal) and organizing environments into controlled train/validation/test sets, MINOS aids in methodical investigation into the relative performance of various RL methodologies. The simulator effectively benchmarks these models against realistic indoor navigation challenges.
3. Empirical Findings: The paper reveals that traditional deep RL methods exhibit limited success within complex, cluttered, and realistic environments. Notably, even the most adept algorithms complete navigation tasks in medium-scale environments only 20% of the time. This emphasizes a clear need for novel approaches to enhance robustness and generalization in realistic settings.
4. Multimodal Sensor Benefits: Experiments conducted with varied sensory modalities underscore the critical role of multimodal input in navigation—depth and touch sensors outperformed standard vision inputs in isolation, particularly in dense environments. The integration of multiple sensor modalities further improves navigation success rates, spotlighting the potential for combining sensory streams to bolster navigation efficacy.

Implications

The development of MINOS represents a significant advancement in toolsets available for indoor navigation research. Practically, this paves the way for more effective development of algorithms suited for real-world applications, such as robotics, AR/VR, and autonomous systems. Theoretically, it extends our understanding of how multisensory integration can be leveraged to solve navigation problems in variable and unpredictable environments.

Future Directions

Future research can build upon the platform provided by MINOS to explore several intriguing avenues. These include the integration of more complex task specifications, such as those involving dynamic changes in the environment or interaction with mobile objects, which more closely mimic real-world scenarios. Improved learning frameworks that better utilize the multimodal information available in MINOS also present a promising direction, potentially involving transfer learning from synthetic simulations to real-world deployments.

In conclusion, with its open-source availability, MINOS lays down a comprehensive foundation for the AI research community, fostering new explorations into robust sensorimotor models for complex environment navigation, fit for both academic inquiry and practical implementation.