CURE: Simulation-Augmented Auto-Tuning in Robotics

• URL: http://arxiv.org/abs/2402.05399v2
• Date: Thu, 5 Sep 2024 18:13:00 GMT
• Title: CURE: Simulation-Augmented Auto-Tuning in Robotics
• Authors: Md Abir Hossen, Sonam Kharade, Jason M. O'Kane, Bradley Schmerl, David Garlan, Pooyan Jamshidi,
• Abstract summary: This paper proposes CURE -- a method that identifies causally relevant configuration options. CURE abstracts the causal relationships between various configuration options and robot performance objectives. We demonstrate the effectiveness and transferability of CURE by conducting experiments in both physical robots and simulation.
• Score: 15.943773140929856
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robotic systems are typically composed of various subsystems, such as localization and navigation, each encompassing numerous configurable components (e.g., selecting different planning algorithms). Once an algorithm has been selected for a component, its associated configuration options must be set to the appropriate values. Configuration options across the system stack interact non-trivially. Finding optimal configurations for highly configurable robots to achieve desired performance poses a significant challenge due to the interactions between configuration options across software and hardware that result in an exponentially large and complex configuration space. These challenges are further compounded by the need for transferability between different environments and robotic platforms. Data efficient optimization algorithms (e.g., Bayesian optimization) have been increasingly employed to automate the tuning of configurable parameters in cyber-physical systems. However, such optimization algorithms converge at later stages, often after exhausting the allocated budget (e.g., optimization steps, allotted time) and lacking transferability. This paper proposes CURE -- a method that identifies causally relevant configuration options, enabling the optimization process to operate in a reduced search space, thereby enabling faster optimization of robot performance. CURE abstracts the causal relationships between various configuration options and robot performance objectives by learning a causal model in the source (a low-cost environment such as the Gazebo simulator) and applying the learned knowledge to perform optimization in the target (e.g., Turtlebot 3 physical robot). We demonstrate the effectiveness and transferability of CURE by conducting experiments that involve varying degrees of deployment changes in both physical robots and simulation.

Related papers

• Robotic warehousing operations: a learn-then-optimize approach to large-scale neighborhood search [84.39855372157616]
  This paper supports robotic parts-to-picker operations in warehousing by optimizing order-workstation assignments, item-pod assignments and the schedule of order fulfillment at workstations. We solve it via large-scale neighborhood search, with a novel learn-then-optimize approach to subproblem generation. In collaboration with Amazon Robotics, we show that our model and algorithm generate much stronger solutions for practical problems than state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-08-29T20:22:22Z)
• Evolutionary Solution Adaption for Multi-Objective Metal Cutting Process Optimization [59.45414406974091]
  We introduce a framework for system flexibility that allows us to study the ability of an algorithm to transfer solutions from previous optimization tasks. We study the flexibility of NSGA-II, which we extend by two variants: 1) varying goals, that optimize solutions for two tasks simultaneously to obtain in-between source solutions expected to be more adaptable, and 2) active-inactive genotype, that accommodates different possibilities that can be activated or deactivated. Results show that adaption with standard NSGA-II greatly reduces the number of evaluations required for optimization to a target goal, while the proposed variants further improve the adaption costs.
  arXiv  Detail & Related papers  (2023-05-31T12:07:50Z)
• Energy-efficient Task Adaptation for NLP Edge Inference Leveraging Heterogeneous Memory Architectures [68.91874045918112]
  adapter-ALBERT is an efficient model optimization for maximal data reuse across different tasks. We demonstrate the advantage of mapping the model to a heterogeneous on-chip memory architecture by performing simulations on a validated NLP edge accelerator.
  arXiv  Detail & Related papers  (2023-03-25T14:40:59Z)
• Visualization and Optimization Techniques for High Dimensional Parameter Spaces [4.111899441919165]
  We propose a novel approach to create an auto-tuning framework for storage systems optimization combining both direct optimization techniques and visual analytics research. Our system was developed in tight collaboration with a group of systems performance researchers and its final effectiveness was evaluated with expert interviews, a comparative user study, and two case studies.
  arXiv  Detail & Related papers  (2022-04-28T23:01:15Z)
• Evolving Pareto-Optimal Actor-Critic Algorithms for Generalizability and Stability [67.8426046908398]
  Generalizability and stability are two key objectives for operating reinforcement learning (RL) agents in the real world. This paper presents MetaPG, an evolutionary method for automated design of actor-critic loss functions.
  arXiv  Detail & Related papers  (2022-04-08T20:46:16Z)
• A distributed, plug-n-play algorithm for multi-robot applications with a priori non-computable objective functions [2.2452191187045383]
  In multi-robot applications, the user-defined objectives of the mission can be cast as a general optimization problem. Standard gradient-descent-like algorithms are not applicable to these problems. We introduce a new algorithm that carefully designs each robot's subcost function, the optimization of which can accomplish the overall team objective.
  arXiv  Detail & Related papers  (2021-11-14T20:40:00Z)
• An End-to-End Differentiable Framework for Contact-Aware Robot Design [37.715596272425316]
  We build an end-to-end differentiable framework for contact-aware robot design. A novel deformation-based parameterization allows for the design of articulated rigid robots with arbitrary, complex geometry. A differentiable rigid body simulator can handle contact-rich scenarios and computes analytical gradients for a full spectrum of kinematic and dynamic parameters.
  arXiv  Detail & Related papers  (2021-07-15T17:53:44Z)
• Evolutionary Gait Transfer of Multi-Legged Robots in Complex Terrains [14.787379075870383]
  This paper proposes a transfer learning-based evolutionary framework for gait optimization, named Tr-GO. The idea is to initialize a high-quality population by using the technique of transfer learning, so any kind of population-based optimization algorithms can be seamlessly integrated into this framework. The experimental results show the effectiveness of the proposed framework for the gait optimization problem based on three multi-objective evolutionary algorithms.
  arXiv  Detail & Related papers  (2020-12-24T16:41:36Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)
• Bayesian Optimization for Developmental Robotics with Meta-Learning by Parameters Bounds Reduction [6.19424794628672]
  We present a developmental framework based on long-term memory and reasoning modules (Bayesian optimisation, visual similarity and parameters bounds reduction) We used a simulator to create bin-picking tasks for 8 different objects (7 in simulation and one with real setup, without and with meta-learning with experiences coming from other similar objects) achieving goods results despite a very small optimization budget, with a better performance reached when meta-learning is used.
  arXiv  Detail & Related papers  (2020-07-30T10:55:56Z)
• EOS: a Parallel, Self-Adaptive, Multi-Population Evolutionary Algorithm for Constrained Global Optimization [68.8204255655161]
  EOS is a global optimization algorithm for constrained and unconstrained problems of real-valued variables. It implements a number of improvements to the well-known Differential Evolution (DE) algorithm. Results prove that EOSis capable of achieving increased performance compared to state-of-the-art single-population self-adaptive DE algorithms.
  arXiv  Detail & Related papers  (2020-07-09T10:19:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.