Reset-free Trial-and-Error Learning for Robot Damage Recovery

The high probability of hardware failures prevents many advanced robots (e.g., legged robots) from being confidently deployed in real-world situations (e.g., post-disaster rescue). Instead of attempting to diagnose the failures, robots could adapt by trial-and-error in order to be able to complete their tasks. In this situation, damage recovery can be seen as a Reinforcement Learning (RL) problem. However, the best RL algorithms for robotics require the robot and the environment to be reset to an initial state after each episode, that is, the robot is not learning autonomously. In addition, most of the RL methods for robotics do not scale well with complex robots (e.g., walking robots) and either cannot be used at all or take too long to converge to a solution (e.g., hours of learning). In this paper, we introduce a novel learning algorithm called "Reset-free Trial-and-Error" (RTE) that (1) breaks the complexity by pre-generating hundreds of possible behaviors with a dynamics simulator of the intact robot, and (2) allows complex robots to quickly recover from damage while completing their tasks and taking the environment into account. We evaluate our algorithm on a simulated wheeled robot, a simulated six-legged robot, and a real six-legged walking robot that are damaged in several ways (e.g., a missing leg, a shortened leg, faulty motor, etc.) and whose objective is to reach a sequence of targets in an arena. Our experiments show that the robots can recover most of their locomotion abilities in an environment with obstacles, and without any human intervention.Comment: 18 pages, 16 figures, 3 tables, 6 pseudocodes/algorithms, video at https://youtu.be/IqtyHFrb3BU, code at https://github.com/resibots/chatzilygeroudis_2018_rt

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Adaptive neuro-fuzzy technique for autonomous ground vehicle navigation

This article proposes an adaptive neuro-fuzzy inference system (ANFIS) for solving navigation problems of an autonomous ground vehicle (AGV). The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV in order to reach the target position; and other two ANFIS controllers are used for optimal heading adjustment in order to avoid obstacles. The two velocity controllers receive three sensor inputs: front distance (FD); right distance (RD) and left distance (LD) for the low-level motion control. Two heading controllers deploy the angle difference (AD) between the heading of AGV and the angle to the target to choose the optimal direction. The simulation experiments have been carried out under two different scenarios to investigate the feasibility of the proposed ANFIS technique. The simulation results have been presented using MATLAB software package; showing that ANFIS is capable of performing the navigation and path planning task safely and efficiently in a workspace populated with static obstacles

Intelligent manipulation technique for multi-branch robotic systems

New analytical development in kinematics planning is reported. The INtelligent KInematics Planner (INKIP) consists of the kinematics spline theory and the adaptive logic annealing process. Also, a novel framework of robot learning mechanism is introduced. The FUzzy LOgic Self Organized Neural Networks (FULOSONN) integrates fuzzy logic in commands, control, searching, and reasoning, the embedded expert system for nominal robotics knowledge implementation, and the self organized neural networks for the dynamic knowledge evolutionary process. Progress on the mechanical construction of SRA Advanced Robotic System (SRAARS) and the real time robot vision system is also reported. A decision was made to incorporate the Local Area Network (LAN) technology in the overall communication system

A general learning co-evolution method to generalize autonomous robot navigation behavior

Congress on Evolutionary Computation. La Jolla, CA, 16-19 July 2000.A new coevolutive method, called Uniform Coevolution, is introduced, to learn weights for a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collision avoidance. The coevolutive method allows the evolution of the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with or without coevolution have been tested in a set of environments and the capability for generalization has been shown for each learned behavior. A simulator based on the mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to example-based problems