Certifiable Robot Design Optimization using Differentiable Programming (2204.10935v1)

Abstract: There is a growing need for computational tools to automatically design and verify autonomous systems, especially complex robotic systems involving perception, planning, control, and hardware in the autonomy stack. Differentiable programming has recently emerged as powerful tool for modeling and optimization. However, very few studies have been done to understand how differentiable programming can be used for robust, certifiable end-to-end design optimization. In this paper, we fill this gap by combining differentiable programming for robot design optimization with a novel statistical framework for certifying the robustness of optimized designs. Our framework can conduct end-to-end optimization and robustness certification for robotics systems, enabling simultaneous optimization of navigation, perception, planning, control, and hardware subsystems. Using simulation and hardware experiments, we show how our tool can be used to solve practical problems in robotics. First, we optimize sensor placements for robot navigation (a design with 5 subsystems and 6 tunable parameters) in under 5 minutes to achieve an 8.4x performance improvement compared to the initial design. Second, we solve a multi-agent collaborative manipulation task (3 subsystems and 454 parameters) in under an hour to achieve a 44% performance improvement over the initial design. We find that differentiable programming enables much faster (32% and 20x, respectively for each example) optimization than approximate gradient methods. We certify the robustness of each design and successfully deploy the optimized designs in hardware. An open-source implementation is available at https://github.com/MIT-REALM/architect

• The paper introduces a novel framework combining differentiable programming with a statistical certification method for robust robot design optimization.
• It leverages exact gradients through automatic differentiation, significantly reducing evaluation time compared to traditional methods.
• Empirical results show an 8.4x improvement in sensor placement and a 44% performance boost in multi-agent tasks, validated on hardware.

Certifiable Robot Design Optimization using Differentiable Programming

The paper authored by Charles Dawson and Chuchu Fan presents a novel approach to robot design optimization and robustness certification using differentiable programming, aiming to address the growing need for computational tools in designing and verifying autonomous systems. Robotics design entails interdependent subsystems like perception, planning, control, and hardware, each with numerous parameters requiring fine-tuning for optimal performance. Traditional ad-hoc methods rely heavily on experience, often lacking in comprehensive automation and robustness due to the high complexity and uncertainty involved.

Overview of the Approach

Differentiable programming serves as the core of this framework, offering an efficient mechanism to optimize robot designs leveraging exact gradients. A key feature in the paper's approach is the coupling of this optimization process with a statistical framework to certify the robustness of the optimized designs against environmental uncertainties.

• Differentiable Programming for Optimization: This method views engineering designs as parameterized programs and utilizes automatic differentiation (AD) to compute gradients of performance metrics concerning design parameters. This approach significantly reduces evaluation time compared to approximate gradient methods like finite differences.
• Statistical Framework for Certification: Complementing differentiation-based optimization is a novel statistical methodology grounded in extreme value theory, which estimates the worst-case performance and sensitivity of designs. This analysis considers variability in exogenous parameters such as environmental factors, ensuring that the optimized design remains robust under uncertainty.

Empirical Results and Validation

The authors validate their framework through two case studies:

1. Sensor Placement for Navigation: This involves optimizing the placement of range sensors and feedback controller gains for an autonomous ground vehicle (AGV) navigating through obstacles. The optimization achieved an 8.4x improvement over the initial design in under five minutes. Hardware validation confirmed the simulation results, showcasing reduced drift and improved localization accuracy.
2. Multi-Agent Collaborative Manipulation: This problem optimizes a neural network and controller parameters for two robots collaboratively pushing a box to a target pose. Handling 454 design parameters, the optimization showed a 44% performance enhancement over the initial design, achieved in under an hour. The framework efficiently customized trajectories for different desired poses, verified through hardware implementation.

Implications and Future Developments

The proposed approach provides a comprehensive and generalizable toolkit for robotics engineers, advocating the value of differentiable programming as an effective alternative to conventional gradient approximation methods, especially in complex, high-dimensional spaces. The fusion of optimization and robustness certification positions the tool as a crucial asset in accelerating the development and deployment of reliable robotic systems.

Moving forward, the paper suggests exploring enhancements such as integrating adversarial training for better handling of rare events and extending the framework to accommodate discrete design variables alongside continuous ones. By expanding the software library with more components such as advanced robotics algorithms, the authors hope to broaden the application spectrum, including robotic arms and autonomous vehicles.

Conclusion

This research presents a significant stride in robotics design, unifying model-based optimization with rigorous statistical analysis to ensure robust performance. Its implementation holds potential to redefine the workflow in robotics engineering, shifting from labor-intensive parameter tuning to automated, data-driven decision-making processes. As future iterations enhance its capabilities, this approach could evolve into a standard for designing complex, autonomous robotic systems efficiently and reliably.