Towards real-world navigation with deep differentiable planners (2108.05713v2)

Abstract: We train embodied neural networks to plan and navigate unseen complex 3D environments, emphasising real-world deployment. Rather than requiring prior knowledge of the agent or environment, the planner learns to model the state transitions and rewards. To avoid the potentially hazardous trial-and-error of reinforcement learning, we focus on differentiable planners such as Value Iteration Networks (VIN), which are trained offline from safe expert demonstrations. Although they work well in small simulations, we address two major limitations that hinder their deployment. First, we observed that current differentiable planners struggle to plan long-term in environments with a high branching complexity. While they should ideally learn to assign low rewards to obstacles to avoid collisions, we posit that the constraints imposed on the network are not strong enough to guarantee the network to learn sufficiently large penalties for every possible collision. We thus impose a structural constraint on the value iteration, which explicitly learns to model any impossible actions. Secondly, we extend the model to work with a limited perspective camera under translation and rotation, which is crucial for real robot deployment. Many VIN-like planners assume a 360 degrees or overhead view without rotation. In contrast, our method uses a memory-efficient lattice map to aggregate CNN embeddings of partial observations, and models the rotational dynamics explicitly using a 3D state-space grid (translation and rotation). Our proposals significantly improve semantic navigation and exploration on several 2D and 3D environments, succeeding in settings that are otherwise challenging for this class of methods. As far as we know, we are the first to successfully perform differentiable planning on the difficult Active Vision Dataset, consisting of real images captured from a robot.

• The paper introduces an augmented transition model that enforces safe navigation by explicitly modeling illegal maneuvers.
• The paper extends planning to 3D embodied states by incorporating precise orientation control, overcoming VIN limitations.
• The paper validates its approach on real-world datasets, achieving significantly higher success rates in complex navigation tasks.

Analyzing Deep Differentiable Planners for Real-World Navigation

The paper "Towards real-world navigation with deep differentiable planners" by Shu Ishida and João F. Henriques explores the training of embodied neural networks for navigation and planning in complex 3D environments. The authors aim to address the constraints of current differentiable planners like Value Iteration Networks (VINs) by proposing enhancements that enable deployment in real-world scenarios.

The research identifies two significant limitations of existing differentiable planners. Firstly, VINs lack the ability to plan effectively in environments with high branching complexity, often failing to assign strong negative rewards to obstacles to deter collisions. Secondly, real-world applications necessitate systems that can handle limited perspective cameras under translation and fine rotations.

The authors introduce several key innovations:

1. Augmented Transition Model: A constrained transition model is proposed, which integrates a probabilistic framework to explicitly model illegal actions and task termination, aiming for safe navigation without trial-and-error learning. This model includes a structural constraint that aids the learning process by modeling impossible actions more effectively.
2. Embodied Planning in 3D State-Space: The model is expanded to accommodate the robot's orientation in addition to its position. This is crucial for embodied navigation as it allows the model to plan through fine-grained orientations, which was often overlooked in previous approaches.
3. Trajectory Reweighting for Balanced Training: Addressing the natural imbalance in navigation training distributions due to exploration and exploitation dynamics, the authors propose a trajectory reweighting scheme. This scheme rebalances the data used during training to enhance model performance.
4. Application to Real-world Data: The paper demonstrates the approach on the Active Vision Dataset, showcasing its applicability to environments with images from real robots. This is a notable advancement as prior work often relied upon simulated environments with unlimited data collection capabilities.

Numerical Results and Claims

Through rigorous experimentation across various settings, the authors substantiate their claims with empirical evidence. For instance, in partially observable 2D mazes— which pose significant challenges to planners— the proposed approach achieves a success rate significantly higher than traditional VINs and other contemporary models. In more complex 3D scenarios such as the MiniWorld simulator and the challenging Active Vision Dataset, the system again outperforms alternatives, indicating robust generalization and efficient exploration capabilities.

Implications and Future Directions

The implications of this research are multifaceted. Practically, the approach offers a framework for robotic systems that require efficient navigation in environments with unknown elements, enhancing the adaptability and safety of such systems without extensive trial-and-error learning. Theoretically, this work contributes to the discussion on merging classical planning algorithms with modern deep learning techniques, showcasing how constraints and probabilistic reasoning can be integrated into neural architectures.

Looking forward, there are various avenues for extended research. Future work could delve into incorporating more dynamic sensor inputs to handle real-time environmental changes, or exploring robustness in even more diverse and unstructured environments. The potential for integrating LLMs to enable natural language guided navigation tasks also holds promise. Finally, further exploration into mitigating the inherent unreliability of neural methods in safety-critical contexts remains a crucial area for development.

In essence, this paper delivers substantial contributions toward the realization of deep differentiable planners capable of real-world navigation, bridging the gap between simulation and practical utilisation.