Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Smart Placement, Faster Robots -- A Comparison of Algorithms for Robot Base-Pose Optimization

Matthias Mayer, Matthias Althoff

TL;DR

This paper tackles the base pose optimization problem for industrial robots and reports a systematic comparison of four optimization strategies—exhaustive search, genetic algorithms, Bayesian optimization, and stochastic gradient descent—across a common set of CoBRA-based benchmarks. The authors adapt SGD with Adam to the baseopt setting, thoroughly tune hyperparameters, and evaluate generalization to varied tasks including 3D-scanned environments. Key findings show SGD attaining the highest task-success rates on real-world-like tasks (over 90%), GA achieving the lowest final costs in several scenarios, and BO often underperforming relative to the other methods. The work provides a reproducible suite of benchmarks and baselines, enabling fair comparisons for future baseopt approaches and facilitating more productive robot deployments in variable environments.

Abstract

Robotic automation is a key technology that increases the efficiency and flexibility of manufacturing processes. However, one of the challenges in deploying robots in novel environments is finding the optimal base pose for the robot, which affects its reachability and deployment cost. Yet, the existing research for automatically optimizing the base pose of robots has not been compared. We address this problem by optimizing the base pose of industrial robots with Bayesian optimization, exhaustive search, genetic algorithms, and stochastic gradient descent and find that all algorithms can reduce the cycle time for various evaluated tasks in synthetic and real-world environments. Stochastic gradient descent shows superior performance with regard to success rate solving over 90% of our real-world tasks, while genetic algorithms show the lowest final costs. All benchmarks and implemented methods are available as baselines against which novel approaches can be compared.

Smart Placement, Faster Robots -- A Comparison of Algorithms for Robot Base-Pose Optimization

TL;DR

This paper tackles the base pose optimization problem for industrial robots and reports a systematic comparison of four optimization strategies—exhaustive search, genetic algorithms, Bayesian optimization, and stochastic gradient descent—across a common set of CoBRA-based benchmarks. The authors adapt SGD with Adam to the baseopt setting, thoroughly tune hyperparameters, and evaluate generalization to varied tasks including 3D-scanned environments. Key findings show SGD attaining the highest task-success rates on real-world-like tasks (over 90%), GA achieving the lowest final costs in several scenarios, and BO often underperforming relative to the other methods. The work provides a reproducible suite of benchmarks and baselines, enabling fair comparisons for future baseopt approaches and facilitating more productive robot deployments in variable environments.

Abstract

Robotic automation is a key technology that increases the efficiency and flexibility of manufacturing processes. However, one of the challenges in deploying robots in novel environments is finding the optimal base pose for the robot, which affects its reachability and deployment cost. Yet, the existing research for automatically optimizing the base pose of robots has not been compared. We address this problem by optimizing the base pose of industrial robots with Bayesian optimization, exhaustive search, genetic algorithms, and stochastic gradient descent and find that all algorithms can reduce the cycle time for various evaluated tasks in synthetic and real-world environments. Stochastic gradient descent shows superior performance with regard to success rate solving over 90% of our real-world tasks, while genetic algorithms show the lowest final costs. All benchmarks and implemented methods are available as baselines against which novel approaches can be compared.
Paper Structure (16 sections, 3 equations, 2 figures)

This paper contains 16 sections, 3 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. es
  4. Capability maps
  5. ga
  6. bo
  7. Benchmarks
  8. Contributions
  9. Methods
  10. Baseline and Exhaustive Search (ES)
  11. Genetic Algorithms (GAs)
  12. Bayesian Optimization (BO)
  13. Stochastic Gradient Decent (SGD)
  14. Task Solver
  15. Numerical Experiments
  16. ...and 1 more sections

Figures (2)

  • Figure 1: Visualization of the optimized base pose $\mathbf{B}^*$ of robot\ref{['fn:robot_spec']}$R$ solving a point-to-point movement in a 3D-scanned environment. The trajectory $x(t)$ connects the four goal poses $g_{0..3}$ drawn as coordinate frames. We indicate the allowed base positions of the robot $\mathcal{B}$ in 3D space by the green outline of a box.
  • Figure 2: Order of filters to test if the base pose is feasible and calculate the cost at that pose.