Latent Space Planning for Stochastic Systems: An Efficient Approach to Risk-Bounded Trajectory Optimization with Learned Dynamics

Conceitos Básicos

A "generate-and-test" approach to risk-bounded planning for autonomous mobile agents with learned, non-linear, stochastic dynamics. The method uses a variational autoencoder to learn an approximate linear latent dynamics model and performs trajectory optimization in the latent space, while a validator assesses the risk of the candidate trajectory and computes additional safety constraints.

Resumo

The paper introduces Latent Space Planning for Stochastic Systems (LaPlaSS), an algorithm for risk-bounded planning for non-linear stochastic agents with learned dynamics. The approach employs a "generate-and-test" strategy:

1. The trajectory planner generates a candidate control trajectory using an approximate linear dynamics model in the latent space learned via a variational autoencoder (VAE).
2. The trajectory validator samples trajectories around the candidate using the accurate, stochastic VAE dynamics model to compute a probabilistic flow tube. It then assesses the risk of collision with obstacles and computes a safety constraint if the risk bound is violated.
3. The safety constraint is passed back to the trajectory planner, which refines the candidate trajectory in the next iteration.
4. The process iterates until a safe trajectory is found or the problem is deemed infeasible.

The key components are:

• Learning an accurate stochastic dynamics model using a VAE to capture uncertainty.
• Learning an approximate linear dynamics model in the latent space of the VAE for efficient trajectory optimization.
• Encoding the planning problem into the latent space to leverage convex optimization techniques.
• The "generate-and-test" approach that iterates between planning and validation to ensure risk-bounded trajectories.

The authors demonstrate that their algorithm can generate safe trajectories an order of magnitude faster than the state-of-the-art approach for agents with known dynamics, and show successful planning for a real-world autonomous drone with learned dynamics.

Estatísticas

The authors evaluate their approach on a simulated non-linear stochastic agent with known dynamics, as well as a real-world autonomous drone dataset.
For the simulated agent:

The average time to generate a safe trajectory was 11 seconds, compared to over 120 seconds for the state-of-the-art approach.
The average total risk of the solution was 0.032, compared to 0.023 for the state-of-the-art.
The average objective value (sum of squared velocity and steering angle) was 85.28, compared to 74.16 for the state-of-the-art.
For the real-world drone dataset:

The authors successfully generated a safe trajectory plan for the quadrotor without prior knowledge of the dynamics.
The final trajectory respected the control limits, avoided ellipsoidal obstacles, and took the agent from the initial state to the goal.

Citações

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Principais Insights Extraídos De

LaPlaSS

by Marlyse Reev... às arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.07063.pdf

Perguntas Mais Profundas

How could the learned latent dynamics model be further improved to better capture the nonlinearities and uncertainties of the real-world system

To enhance the learned latent dynamics model's capability in capturing the complexities of real-world systems, several improvements can be implemented. Firstly, incorporating more sophisticated neural network architectures, such as recurrent neural networks (RNNs) or transformers, can enable the model to better capture temporal dependencies and long-range interactions in the system dynamics. Additionally, introducing attention mechanisms can help the model focus on relevant parts of the input data, improving its ability to handle nonlinearities and uncertainties. Furthermore, utilizing ensemble methods or Bayesian neural networks can provide a more robust estimation of the latent dynamics, considering the inherent uncertainties in the system. Lastly, integrating domain knowledge or physical constraints into the model training process can guide the learning towards more realistic and interpretable latent representations of the system dynamics.

What other types of constraints or objectives could be incorporated into the latent space planning formulation to address a wider range of autonomous agent applications

Incorporating additional constraints and objectives into the latent space planning formulation can broaden the applicability of the approach to various autonomous agent scenarios. One key extension could be the integration of energy efficiency constraints to optimize the agent's trajectory while minimizing energy consumption. Safety constraints related to collision avoidance with moving obstacles or dynamic environments can also be included to ensure safe navigation. Moreover, incorporating constraints on communication bandwidth or resource utilization can be crucial for multi-agent systems or networked robots. Objectives like task prioritization, multi-objective optimization, or adaptive goal achievement could further enhance the versatility of the planning framework, allowing for more flexible and adaptive agent behavior in diverse environments.

How could the proposed approach be extended to handle partially observable environments or incorporate online sensor feedback during execution of the planned trajectory

To handle partially observable environments or incorporate online sensor feedback during trajectory execution, the proposed approach can be extended in several ways. One approach is to integrate techniques from reinforcement learning, such as model predictive control with reinforcement learning, to enable the agent to adapt its trajectory based on real-time sensor information. Utilizing Bayesian inference methods can help in updating the latent dynamics model with new sensor data, improving the model's accuracy and adaptability. Additionally, incorporating techniques like particle filtering or Kalman filtering can enable the agent to estimate the unobserved states and uncertainties in the environment, enhancing its decision-making capabilities in partially observable settings. By combining these approaches, the planning framework can effectively handle dynamic and uncertain environments, ensuring robust and adaptive autonomous agent behavior.