In outdoor environments, mobile robots are required to navigate through terrain with varying characteristics, some of which might significantly affect the integrity of the platform. Ideally, the robot should be able to identify areas that are safe for navigation based on its own percepts about the environment while avoiding damage to itself. Bayesian optimisation (BO) has been successfully applied to the task of learning a model of terrain traversability while guiding the robot through more traversable areas. An issue, however, is that localisation uncertainty can end up guiding the robot to unsafe areas and distort the model being learnt. In this paper, we address this problem and present a novel method that allows BO to consider localisation uncertainty by applying a Gaussian process model for uncertain inputs as a prior. We evaluate the proposed method in simulation and in experiments with a real robot navigating over rough terrain and compare it to standard BO methods which assume deterministic inputs.
@cite_11 presented a method to learn a GP model for terrain roughness from vehicle experience. The authors applied Bayesian optimisation (BO) @cite_3 in an active perception approach to reduce experienced vibration during navigation while learning the model from IMU measurements online. In the BO framework, the terrain roughness model is learnt online as the algorithm drives the robot around selecting locations to visit balancing a trade-off between exploration and exploitation @cite_3 @cite_11 . Nevertheless, BO usually considers deterministic query locations within its search space, as BO is typically used in problems where a fixed number of parameters have to be optimised @cite_25 @cite_17 .
@cite_17 @cite_25 @cite_3 @cite_11
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In future planetary exploration missions, improvements in autonomous rover mobility have the potential to increase scientific data return by providing safe access to geologically interesting sites that lie in rugged terrain, far from landing areas. To improve rover-based terrain sensing, this paper proposes a self-supervised learning framework that will enable a robotic system to learn to predict mechanical properties of distant terrain, based on measurements of mechanical properties of similar terrain that has been traversed previously. In this framework, a proprioceptive terrain classifier is used to distinguish terrain classes based on features derived from rover–terrain interaction, and labels from this classifier are used to train an exteroceptive (i.e., vision-based) terrain classifier. Once trained, the vision-based classifier is able to recognize similar terrain classes in stereo imagery. This paper presents two distinct proprioceptive classifiers—a novel approach based on optimization of a traction force model and a previously described approach based on wheel vibration—as well as a vision-based terrain classification approach suitable for environments with unexpected appearances. The high accuracy of the self-supervised learning framework and its supporting algorithms is demonstrated using experimental data from a four-wheeled robot in an outdoor Mars-analogue environment. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc. Recently, Bayesian Optimization (BO) has been used to successfully optimize parametric policies in several challenging Reinforcement Learning (RL) applications. BO is attractive for this problem because it exploits Bayesian prior information about the expected return and exploits this knowledge to select new policies to execute. Effectively, the BO framework for policy search addresses the exploration-exploitation tradeoff. In this work, we show how to more effectively apply BO to RL by exploiting the sequential trajectory information generated by RL agents. Our contributions can be broken into two distinct, but mutually beneficial, parts. The first is a new Gaussian process (GP) kernel for measuring the similarity between policies using trajectory data generated from policy executions. This kernel can be used in order to improve posterior estimates of the expected return thereby improving the quality of exploration. The second contribution, is a new GP mean function which uses learned transition and reward functions to approximate the surface of the objective. We show that the model-based approach we develop can recover from model inaccuracies when good transition and reward models cannot be learned. We give empirical results in a standard set of RL benchmarks showing that both our model-based and model-free approaches can speed up learning compared to competing methods. Further, we show that our contributions can be combined to yield synergistic improvement in some domains. The varied and complex dynamics of deformable terrain are significant impediments toward real-world viability of locomotive robotics, particularly for legged machines. We explore vertical jumping on granular media (GM) as a model task for legged locomotion on uncharacterized deformable terrain. By integrating (Gaussian process) GP-based regression and evaluation to estimate ground forcing as a function of state, a one-dimensional jumper acquires the ability to learn forcing profiles exerted by its environment in tandem to achieving its control objective. The GP-based dynamical model initially assumes a baseline rigid, non-compliant surface. As part of an iterative procedure, the optimizer employing this model generates an optimal control to achieve a target jump height while respecting known hardware limitations of the robot model. Trajectory and forcing data recovered from evaluation on the true GM surface model simulation is applied to train the GP, and in turn, provide the optimizer a more richly informed dynamical model of the environment. After three iterations, predicted optimal control trajectories coincide with execution results, within 1.2 jumping height error, as the GP-based approximation converges to the true GM model. A key challenge for long-term autonomy is to enable a robot to automatically model properties of the environment while actively searching for better decisions to accomplish its task. This amounts to the problem of exploration-exploitation in the context of active perception. This paper addresses active perception and presents a technique to incrementally model the roughness of the terrain a robot navigates on while actively searching for paths that reduce the overall vibration experienced during travel. The approach employs Gaussian processes in conjunction with Bayesian optimisation for decision making. The algorithms are executed in real-time on the robot while it explores the environment. We present experiments with an outdoor vehicle navigating over several types of terrains demonstrating the properties and effectiveness of the approach.