Unsupervised learning techniques, such as Bayesian topic models, are capable of discovering latent structure directly from raw data. These unsupervised models can endow robots with the ability to learn from their observations without human supervision, and then use the learned models for tasks such as autonomous exploration, adaptive sampling, or surveillance. This paper extends single-robot topic models to the domain of multiple robots. The main difficulty of this extension lies in achieving and maintaining global consensus among the unsupervised models learned locally by each robot. This is especially challenging for multi-robot teams operating in communication-constrained environments, such as marine robots.
We present a novel approach for multi-robot distributed learning in which each robot maintains a local topic model to categorize its observations and model parameters are shared to achieve global consensus. We apply a combinatorial optimization procedure that combines local robot topic distributions into a globally consistent model based on topic similarity, which we find mitigates topic drift when compared to a baseline approach that matches topics naively. We evaluate our methods experimentally by demonstrating multi-robot underwater terrain characterization using simulated missions on real seabed imagery. Our proposed method achieves similar model quality under bandwidth-constraints to that achieved by models that continuously communicate, despite requiring less than one percent of the data transmission needed for continuous communication.
more »
« less
Streaming Scene Maps for Co-Robotic Exploration in Bandwidth Limited Environments
This paper proposes a bandwidth tunable technique for real-time probabilistic scene modeling and mapping to enable co-robotic exploration in communication constrained environments such as the deep sea. The parameters of the system enable the user to characterize the scene complexity represented by the map, which in turn determines the bandwidth requirements. The approach is demonstrated using an underwater robot that learns an unsupervised scene model of the environment and then uses this scene model to communicate the spatial distribution of various high-level semantic scene constructs to a human operator. Preliminary experiments in an artificially constructed tank environment, as well as simulated missions over a 10m x 10m coral reef using real data, show the tunability of the maps to different bandwidth constraints and science interests. To our knowledge this is the first paper to quantify how the free parameters of the unsupervised scene model impact both the scientific utility of and bandwidth required to communicate the resulting scene model.
more »
« less
- Award ID(s):
- 1734400
- NSF-PAR ID:
- 10127644
- Date Published:
- Journal Name:
- 2019 International Conference on Robotics and Automation (ICRA)
- Page Range / eLocation ID:
- 7940 to 7946
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
We present an unsupervised learning framework for simultaneously training single-view depth prediction and optical flow estimation models using unlabeled video sequences. Existing unsupervised methods often exploit brightness constancy and spatial smoothness priors to train depth or flow models. In this paper, we propose to leverage geometric consistency as additional supervisory signals. Our core idea is that for rigid regions we can use the predicted scene depth and camera motion to synthesize 2D optical flow by backprojecting the induced 3D scene flow. The discrepancy between the rigid flow (from depth prediction and camera motion) and the estimated flow (from optical flow model) allows us to impose a cross-task consistency loss. While all the networks are jointly optimized during training, they can be applied independently at test time. Extensive experiments demonstrate that our depth and flow models compare favorably with state-of-the-art unsupervised methods.more » « less
-
null (Ed.)more » « less
Learning interpretable representations in an unsupervised setting is an important yet a challenging task. Existing unsupervised interpretable methods focus on extracting independent salient features from data. However they miss out the fact that the entanglement of salient features may also be informative. Acknowledging these entanglements can improve the interpretability, resulting in extraction of higher quality and a wider variety of salient features. In this paper, we propose a new method to enable Generative Adversarial Networks (GANs) to discover salient features that may be entangled in an informative manner, instead of extracting only disentangled features. Specifically, we propose a regularizer to punish the disagreement between the extracted feature interactions and a given dependency structure while training. We model these interactions using a Bayesian network, estimate the maximum likelihood parameters and calculate a negative likelihood score to measure the disagreement. Upon qualitatively and quantitatively evaluating the proposed method using both synthetic and real-world datasets, we show that our proposed regularizer guides GANs to learn representations with disentanglement scores competing with the state-of-the-art, while extracting a wider variety of salient features.
-
more » « less
Abstract Precomputed Radiance Transfer (PRT) remains an attractive solution for real‐time rendering of complex light transport effects such as glossy global illumination. After precomputation, we can relight the scene with new environment maps while changing viewpoint in real‐time. However, practical PRT methods are usually limited to low‐frequency spherical harmonic lighting. All‐frequency techniques using wavelets are promising but have so far had little practical impact. The curse of dimensionality and much higher data requirements have typically limited them to relighting with fixed view or only direct lighting with triple product integrals. In this paper, we demonstrate a hybrid neural‐wavelet PRT solution to high‐frequency indirect illumination, including glossy reflection, for relighting with changing view. Specifically, we seek to represent the light transport function in the Haar wavelet basis. For global illumination, we learn the wavelet transport using a small multi‐layer perceptron (MLP) applied to a feature field as a function of spatial location and wavelet index, with reflected direction and material parameters being other MLP inputs. We optimize/learn the feature field (compactly represented by a tensor decomposition) and MLP parameters from multiple images of the scene under different lighting and viewing conditions. We demonstrate real‐time (512 x 512 at 24 FPS, 800 x 600 at 13 FPS) precomputed rendering of challenging scenes involving view‐dependent reflections and even caustics.
-
The ability for computational agents to reason about the high-level content of real world scene images is important for many applications. Existing attempts at complex scene understanding lack representational power, efficiency, and the ability to create robust meta- knowledge about scenes. We introduce scenarios as a new way of representing scenes. The scenario is an interpretable, low-dimensional, data-driven representation consisting of sets of frequently co-occurring objects that is useful for a wide range of scene under- standing tasks. Scenarios are learned from data using a novel matrix factorization method which is integrated into a new neural network architecture, the Scenari-oNet. Using ScenarioNet, we can recover semantic in- formation about real world scene images at three levels of granularity: 1) scene categories, 2) scenarios, and 3) objects. Training a single ScenarioNet model enables us to perform scene classification, scenario recognition, multi-object recognition, content-based scene image retrieval, and content-based image comparison. ScenarioNet is efficient because it requires significantly fewer parameters than other CNNs while achieving similar performance on benchmark tasks, and it is interpretable because it produces evidence in an understandable format for every decision it makes. We validate the utility of scenarios and ScenarioNet on a diverse set of scene understanding tasks on several benchmark datasets.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICRA.2019.8794132
