Evolutionary Gait Transfer of Multi-Legged Robots in Complex Terrains
- URL: http://arxiv.org/abs/2012.13320v1
- Date: Thu, 24 Dec 2020 16:41:36 GMT
- Title: Evolutionary Gait Transfer of Multi-Legged Robots in Complex Terrains
- Authors: Min Jiang, Guokun Chi, Geqiang Pan, Shihui Guo, and Kay Chen Tan
- Abstract summary: 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.
- Score: 14.787379075870383
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Robot gait optimization is the task of generating an optimal control
trajectory under various internal and external constraints. Given the high
dimensions of control space, this problem is particularly challenging for
multi-legged robots walking in complex and unknown environments. Existing
literatures often regard the gait generation as an optimization problem and
solve the gait optimization from scratch for robots walking in a specific
environment. However, such approaches do not consider the use of pre-acquired
knowledge which can be useful in improving the quality and speed of motion
generation in complex environments. To address the issue, this paper proposes a
transfer learning-based evolutionary framework for multi-objective 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
advantage is that the generated gait can not only dynamically adapt to
different environments and tasks, but also simultaneously satisfy multiple
design specifications (e.g., speed, stability). The experimental results show
the effectiveness of the proposed framework for the gait optimization problem
based on three multi-objective evolutionary algorithms: NSGA-II, RM-MEDA and
MOPSO. When transferring the pre-acquired knowledge from the plain terrain to
various inclined and rugged ones, the proposed Tr-GO framework accelerates the
evolution process by a minimum of 3-4 times compared with non-transferred
scenarios.
Related papers
- Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate [105.86576388991713]
We introduce a normalized gradient difference (NGDiff) algorithm, enabling us to have better control over the trade-off between the objectives.
We provide a theoretical analysis and empirically demonstrate the superior performance of NGDiff among state-of-the-art unlearning methods on the TOFU and MUSE datasets.
arXiv Detail & Related papers (2024-10-29T14:41:44Z) - CURE: Simulation-Augmented Auto-Tuning in Robotics [15.943773140929856]
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.
arXiv Detail & Related papers (2024-02-08T04:27:14Z) - Robustness for Free: Quality-Diversity Driven Discovery of Agile Soft
Robotic Gaits [0.7829600874436199]
We show how Quality Diversity Algorithms can produce repertoires of gaits robust to changing terrains.
This robustness significantly out-performs that of gaits produced by a single objective optimization algorithm.
arXiv Detail & Related papers (2023-11-02T14:00:11Z) - Design Optimizer for Planar Soft-Growing Robot Manipulators [1.1888144645004388]
This work presents a novel approach for design optimization of soft-growing robots.
I optimize the kinematic chain of a soft manipulator to reach targets and avoid unnecessary overuse of material and resources.
I tested the proposed method on different tasks to access its optimality, which showed significant performance in solving the problem.
arXiv Detail & Related papers (2023-10-05T08:23:17Z) - 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) - A Data-Driven Evolutionary Transfer Optimization for Expensive Problems
in Dynamic Environments [9.098403098464704]
Data-driven, a.k.a. surrogate-assisted, evolutionary optimization has been recognized as an effective approach for tackling expensive black-box optimization problems.
This paper proposes a simple but effective transfer learning framework to empower data-driven evolutionary optimization to solve dynamic optimization problems.
Experiments on synthetic benchmark test problems and a real-world case study demonstrate the effectiveness of our proposed algorithm.
arXiv Detail & Related papers (2022-11-05T11:19:50Z) - Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with
Heterogeneous Learning Tasks [53.1636151439562]
Mobile edge computing (MEC) provides a natural platform for AI applications.
We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS)
Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
arXiv Detail & Related papers (2020-12-25T07:08:50Z) - Distributed Multi-agent Meta Learning for Trajectory Design in Wireless
Drone Networks [151.27147513363502]
This paper studies the problem of the trajectory design for a group of energyconstrained drones operating in dynamic wireless network environments.
A value based reinforcement learning (VDRL) solution and a metatraining mechanism is proposed.
arXiv Detail & Related papers (2020-12-06T01:30:12Z) - Bioinspired Bipedal Locomotion Control for Humanoid Robotics Based on
EACO [1.0152838128195467]
This work presents promoting global search capability and convergence rate of the EACO applied to humanoid robots in real-time.
We put a special focus on the EACO algorithm on a wide range of problems, from ACO, real-coded GAs, GAs with neural networks(NNs), particle swarm optimization(PSO) to complex robotics systems.
arXiv Detail & Related papers (2020-10-09T09:43:48Z) - 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) - Automatically Learning Compact Quality-aware Surrogates for Optimization
Problems [55.94450542785096]
Solving optimization problems with unknown parameters requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values.
Recent work has shown that including the optimization problem as a layer in a complex training model pipeline results in predictions of iteration of unobserved decision making.
We show that we can improve solution quality by learning a low-dimensional surrogate model of a large optimization problem.
arXiv Detail & Related papers (2020-06-18T19:11:54Z)
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.