Sensor fusion is indispensable to improve accuracy and robustness in an autonomous navigation setting. However, in the space of end-to-end sensorimotor control, this multimodal outlook has received limited attention. In this work, we propose a novel stochastic regularization technique, called Sensor Dropout, to robustify multimodal sensor policy learning outcomes. We also introduce an auxiliary loss on policy network along with the standard DRL loss that help reduce the action variations of the multimodal sensor policy. Through empirical testing we demonstrate that our proposed policy can 1) operate with minimal performance drop in noisy environments, 2) remain functional even in the face of a sensor subset failure. Finally, through the visualization of gradients, we show that the learned policies are conditioned on the same latent input distribution despite having multiple sensory observations spaces - a hallmark of true sensor-fusion. This efficacy of a multimodal policy is shown through simulations on TORCS, a popular open-source racing car game. A demo video can be seen here: this https URL
Multisensory deep learning, popularly called Multimodal deep learning, is an active area of research in other domains like audiovisual systems @cite_28 , text speech and language models @cite_19 , etc. However, Multi-modal learning is conspicuous by its absence in the modern end-to-end autonomous navigation literature. Another challenge in multimodal learning is the specific case of over-fitting where instead of learning the underlying latent target state representation using multiple diverse observations, the model instead learns a complex representation in the original space itself, defeating the purpose of using multi-sensor observations and making the process computationally burdensome. An illustrative example for this case is a car navigating when all sensors remain functional but fails to navigate at all even if one sensor fails or is partially corrupted. This kind of behavior is detrimental and suitable regularization measures should be set up during training to avoid it.
@cite_28 @cite_19
2184188583 154472438
Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and show how to train deep networks that learn features to address these tasks. In particular, we demonstrate cross modality feature learning, where better features for one modality (e.g., video) can be learned if multiple modalities (e.g., audio and video) are present at feature learning time. Furthermore, we show how to learn a shared representation between modalities and evaluate it on a unique task, where the classifier is trained with audio-only data but tested with video-only data and vice-versa. Our models are validated on the CUAVE and AVLetters datasets on audio-visual speech classification, demonstrating best published visual speech classification on AVLetters and effective shared representation learning. Data often consists of multiple diverse modalities. For example, images are tagged with textual information and videos are accompanied by audio. Each modality is characterized by having distinct statistical properties. We propose a Deep Boltzmann Machine for learning a generative model of such multimodal data. We show that the model can be used to create fused representations by combining features across modalities. These learned representations are useful for classification and information retrieval. By sampling from the conditional distributions over each data modality, it is possible to create these representations even when some data modalities are missing. We conduct experiments on bimodal image-text and audio-video data. The fused representation achieves good classification results on the MIR-Flickr data set matching or outperforming other deep models as well as SVM based models that use Multiple Kernel Learning. We further demonstrate that this multimodal model helps classification and retrieval even when only unimodal data is available at test time.