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Virtual-to-Real-World Transfer Learning for Robots on Wilderness Trails

Michael L. Iuzzolino, Michael E. Walker, Daniel Szafir

arXiv:1901.05599v1cs.LGcs.CVcs.ROstat.ML

TL;DR

Wilderness-trail navigation is difficult, and deep-learning approaches rely on large labeled datasets that can be hard to collect. This paper trains trail-direction classifiers on Unity-generated images and evaluates transfer to real-world trail data, reporting above-baseline accuracies in both domains.

  • Problem

    Wilderness trails vary widely in terrain and conditions, and deep-learning navigation approaches require large labeled datasets that may be difficult to collect.

    Source: §I · p. 1

  • Method

    The authors train trail-direction neural networks on synthetic images from Unity and evaluate them on real-world trail data.

    Source: §I · p. 1, B. Data Collection · p. 2

  • Results

    88%-95% virtual-data and 38%-58% real-world classification accuracies exceeded dataset baselines, including the 33.33% real-world baseline.

    Source: §VI · p. 6

  • Takeaways & Limitations

    Virtual-to-real transfer is a promising approach for addressing real-world data-collection challenges in autonomous robotics.

    Source: §VI · p. 6

    On real-world test sets, the virtually trained models did not outperform CNN or human baselines.

    Source: §V · p. 5

Abstract

from arXiv · show

Robots hold promise in many scenarios involving outdoor use, such as search-and-rescue, wildlife management, and collecting data to improve environment, climate, and weather forecasting. However, autonomous navigation of outdoor trails remains a challenging problem. Recent work has sought to address this issue using deep learning. Although this approach has achieved state-of-the-art results, the deep learning paradigm may be limited due to a reliance on large amounts of annotated training data. Collecting and curating training datasets may not be feasible or practical in many situations, especially as trail conditions may change due to seasonal weather variations, storms, and natural erosion. In this paper, we explore an approach to address this issue through virtual-to-real-world transfer learning using a variety of deep learning models trained to classify the direction of a trail in an image. Our approach utilizes synthetic data gathered from virtual environments for model training, bypassing the need to collect a large amount of real images of the outdoors. We validate our approach in three main ways. First, we demonstrate that our models achieve classification accuracies upwards of 95% on our synthetic data set. Next, we utilize our classification models in the control system of a simulated robot to demonstrate feasibility. Finally, we evaluate our models on real-world trail data and demonstrate the potential of virtual-to-real-world transfer learning.

I. INTRODUCTION

Wilderness trails vary in terrain, vegetation, and conditions, making autonomous navigation difficult, while deep-learning approaches depend on labor-intensive labeled data. The paper explores virtual-to-real transfer for trail perception against a background of prior real-data and simulation-based approaches.

I. INTRODUCTION

  • Wilderness trails vary in features, terrain, biomes, seasons, and lighting; vegetation and obstacles can constrain visibility, while GPS may be unreliable or unavailable.These conditions make outdoor-trail navigation a complex perception and planning problem.Source: §I · p. 1
  • Deep-learning approaches achieve state-of-the-art robot perception and navigation results but require large labeled datasets that can be difficult to collect, especially in hazardous conditions.The introduction gives search-and-rescue during harsh weather as an example.Source: §I · p. 1
  • Virtual-to-real transfer trains trail-direction classifiers on synthetic outdoor images, aiming to reduce exhaustive real-world data collection and labeling.The authors report that features learned on virtual trails can transfer to real-world trail perception.Source: §I · p. 1

II. RELATED WORK

  • Giusti et al. used 24,747 automatically labeled alpine trail images; their CNN reached 85.2%, versus 52.3% for hue-saliency/SVM and 86.5% for humans.The images came from three differently oriented GoPro cameras worn by a hiker.Source: §II · pp. 1–2
  • Prior simulation-trained motion-planning work transferred policies to rooms with static obstacles, whereas this paper explores complex wilderness domains where simulations may be lacking.The authors also note that real-world data collection can face battery-life, fatigue, safety, seasonal, and accessibility constraints.Source: §II · p. 2

III. APPROACH

The approach uses a Unity environment to collect synthetic trail images, trains neural networks on them, and evaluates virtual-to-real transfer and robot integration.

III. APPROACH

  • The study creates a virtual environment for synthetic trail-image collection.Unity serves as the cross-platform 3D game engine for the environment.Source: §III · p. 2
  • It trains three archetypal neural networks using data collected and processed from the virtual environment.The passage describes the model-construction methods as part of the approach.Source: §III · p. 2
  • The trained networks are integrated with Unity and evaluated on real-world data to assess virtual-to-real transfer.The evaluation is intended to demonstrate transfer learning for trail perception.Source: §III · p. 2

A. Virtual Environment

The authors built a Unity alpine scene with a branching dirt-trail network, then collected labeled images as a virtual robot traversed a selected path.

A. Virtual Environment

  • The Unity alpine scene uses natural-object models and dirt trails that branch, curve around obstacles, change elevation, and include ambiguous sections.The paths were designed to share several characteristics with real-world trails.Source: A. Virtual Environment · p. 2

B. Data Collection

  • A virtual robot repeatedly traversed one randomly selected path while three 400 × 400 cameras captured images at 30 FPS with 80° fields of view.The combined camera coverage spanned 180°, with each peripheral camera overlapping the central camera by 30°.Source: B. Data Collection · p. 2
  • The robot collected 20,269 images, labeled center, left, or right according to the camera that captured each image.The dataset contained 6,821 center, 6,829 left, and 6,619 right images.Source: B. Data Collection · p. 2

C. Data Processing

Images were resized and normalized before dataset splitting, and the study examined feed-forward, convolutional, and recurrent network architectures.

C. Data Processing

  • Images were resized to 100 × 100 × 3 pixels and normalized per image and color channel to values between 0 and 1.Resizing reduced processing time and memory consumption; normalization addressed variation in pixel values.Source: C. Data Processing · pp. 2–3
  • Unity-collected images were split into training, validation, and test sets, while the real-world dataset from served as an additional test set for transferability.The passage points to Table I for the split distributions.Source: C. Data Processing · p. 3

D. Model Architectures

  • The study explored standard feed-forward DNN, convolutional CNN, and recurrent RNN architectures.The following subsections describe their hyperparameters and input/output structures.Source: D. Model Architectures · p. 3

1) Deep Neural Network:

The paper compares three neural-network architectures for classifying trail direction from virtual images. They differ in how they represent image input before producing left, center, or right predictions.

1) Deep Neural Network:

  • The DNN flattens each 100×100×3 image into a 30000-element vector and predicts left, center, or right using a three-neuron output layer.Three hidden layers use ReLU activations, and softmax converts the outputs into class probabilities.Source: 1) Deep Neural Network · p. 3

2) Convolutional Neural Network:

  • The CNN applies four convolution, activation, and max-pooling blocks before a 200-neuron fully connected layer and three-class output.Its input is a 100×100×3 image; each convolution uses 32 filters and 4×4 kernels.Source: 2) Convolutional Neural Network · p. 3

3) Recurrent Neural Network:

  • The RNN processes each 100×100×3 image as 100 three-channel sequences, using two GRU layers with 32 hidden units each.Both GRU and LSTM cells were explored; the authors chose GRUs because performance differed negligibly and GRUs were simpler.Source: 3) Recurrent Neural Network · p. 3

E. Training: Loss Functions, Optimizers, and Evaluations

Models were trained with a shared loss, optimizer, and accuracy metric. The Unity integration then used network predictions from a single forward-facing camera to control a virtual robot.

E. Training: Loss Functions, Optimizers, and Evaluations

  • All models used cross-entropy loss, Adam with an initial learning rate of 0.001, and accuracy as the evaluation metric.Accuracy is the ratio of correctly labeled images to the total images in the set.Source: E. Training: Loss Functions,… · p. 3

F. Neural Network Integration with Unity

  • In Unity, a single forward-facing camera captured images at 30 FPS, and the network sent predictions back to the scene via UDP.The Unity environment parsed each packet and moved the virtual robot deterministically based on the prediction.Source: F. Neural Network Integration with Unity · pp. 3–4
  • A center prediction drove straight movement, while left and right predictions slowed forward motion and turned toward the trail.The turn direction accounts for training labels corresponding to the source camera orientation: a left-camera image places the trail on the image’s right side.Source: F. Neural Network Integration with Unity · p. 4

G. Evaluation on Real-World Data

The real-world evaluation tested models trained on virtual data against a balanced set of labeled trail images. Predictions were compared with image labels to calculate accuracy.

G. Evaluation on Real-World Data

  • The real-world test set sampled 4,000 images each for left, center, and right, totaling 12,000 images with a 33% baseline.The images came from the dataset in and were used only to test models trained on virtual data.Source: G. Evaluation on Real-World Data · p. 4
  • Each real-world image was processed using the paper’s image-processing method, classified by a virtually trained model, and scored against its true label.Accuracy was calculated over the test set by comparing each prediction with its true label.Source: G. Evaluation on Real-World Data · p. 4
  • Table II is identified as the summary of model results for the paper’s evaluations.The supplied caption identifies the table’s scope but does not provide its results.Source: Table II · p. 4

IV. RESULTS

The recurrent model led on virtual test images, while the deep feed-forward model led on real-world images. In Unity trials, the RNN generally navigated novel trails but sometimes followed trail-like terrain features off-path.

IV. RESULTS

  • The reported virtual and real-world baselines were 35.25% and 33.33%, respectively; model training used 50 epochs.Training times were 1h:23m for the DNN, 9h:12m for the CNN, and 2h:4m for the RNN.Source: §IV · p. 4

A. Virtual Dataset Results

  • On the virtual dataset, the RNN achieved the best result with 95.02% test-set accuracy; on the real-world datasets, the DNN achieved the best result, scoring 58.41%.All three models scored higher than dataset baselines in both virtual and real-world evaluations.Source: A. Virtual Dataset Results · p. 4

B. Unity Follow-Up Evaluations

  • The virtual robot was largely successful on trails not used for training, including tight turns and large boulders.The RNN served as controller because it performed best on the virtual dataset.Source: B. Unity Follow-Up Evaluations · p. 5
  • The robot sometimes left the trail when ridgelines with trail-like features prompted it to follow pseudo-trails.The authors suggest that higher-quality terrain packs could help distinguish trails from visually similar objects.Source: B. Unity Follow-Up Evaluations · p. 5

C. Real-World Dataset Evaluations

Models trained only on virtual trails transferred to real images, with the DNN outperforming a real-trained saliency/SVM baseline. The discussion identifies overfitting, sequence-order sensitivity, image-region cues, and terrain realism as important qualifications.

C. Real-World Dataset Evaluations

  • 58.41%, 38.60%, and 48.51% were the real-world accuracies for DNN, CNN, and RNN, respectively; the DNN exceeded the 52.3% saliency/SVM baseline but not the cited DNN or human baselines.The saliency/SVM baseline was trained on the real-world test set.Source: C. Real-World Dataset Evaluations · p. 5

V. DISCUSSION

  • 88.7%–95.02% virtual accuracy and 38.60%–58.41% real-world accuracy exceeded their respective dataset baselines; the DNN beat the saliency/SVM baseline by more than 6%.The real-world dataset baseline was 33.33%.Source: §V · p. 5
  • Longer training often reduced real-world accuracy, suggesting overfitting on virtual data; the authors propose dropout or interspersed real-world batches as regularization.The proposed fusion approach would introduce real-world data every N virtual-data batches.Source: §V · p. 5
  • Reversing the RNN’s image-reading order substantially reduced virtual-test accuracy, consistent with vanishing gradients degrading salient lower-image information.The authors report that most salient trail-perception features lie in the bottom two-thirds of an image.Source: §V · p. 5
  • The authors suggest that training on only the bottom half or two-thirds of images may reduce processing time and model parameters.This suggestion follows their interpretation that the classifier uses features in the lower half of the image.Source: §V · p. 5
  • Errors on images containing multiple trails suggest that robot decisions may need higher-level goal-position context, such as GPS or compass information.Source: §V · p. 5
  • Low-quality terrain packs can obscure fine distinctions between trails and similar-looking objects, motivating use of higher-quality packs.Source: §V · p. 6
  • The authors expect greater virtual-scene realism to play a direct role in transfer-learning accuracy.Source: §V · p. 6

A. Future Work

Future work proposes broader synthetic terrain generation, more photorealistic data, and feature-isolation experiments. The automated collection process already produces labeled images rapidly.

A. Future Work

  • The model was trained in a virtual alpine environment and tested on similar real terrain; the authors expect worse performance when terrain characteristics and trail features do not match.Future procedural terrain generation would vary weather, lighting, biome, path appearance, elevation, and flora.Source: A. Future Work · p. 6
  • 20,269 images were captured in less than five minutes (4,053 images per minute), versus 24,474 images collected over eight hours.Future work may combine automated collection with procedurally generated, more photorealistic terrain.Source: A. Future Work · p. 6
  • The authors propose turning virtual-scene features off individually and measuring classification changes to identify features important to the task.Repeating the experiment across terrains could reveal globally important features for procedural terrain generation.Source: A. Future Work · p. 6

VI. CONCLUSION

Three neural-network architectures trained on Unity-generated virtual data achieved 88%–95% virtual classification accuracy and 38%–58% real-world accuracy, above a 33.33% baseline. The authors present virtual-to-real transfer as promising for trail navigation where real data collection is challenging, including dangerous or inaccessible terrain.

VI. CONCLUSION

  • 88%–95% virtual-data classification accuracy and 38%–58% real-world classification accuracy exceeded the 33.33% baseline across three neural-network architectures.The virtual data were generated from Unity.Source: §VI · p. 6
  • Virtual label generation may circumvent battery-life, human-fatigue, and safety challenges associated with manual data collection.The authors propose that virtual training could support navigation of hard-to-access or dangerous terrain, including novel environments such as Mars.Source: §VI · p. 6
  • The authors conclude that virtual-to-real-world transfer learning is promising for overcoming challenges in real-world data collection and autonomous robotics development.They describe potential use on terrain that is dangerous or impossible to access and collect real data from.Source: §VI · p. 6
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