3 accepted papers
Autonomous robots, autonomous vehicles, and humans wearing mixed-reality headsets require accurate and reliable tracking services for safety-critical applications in dynamically changing real-world environments. However, the existing tracking approaches, such as Simultaneous Localization and Mapping…
Explaining the inner workings of deep neural network models have received considerable attention in recent years. Researchers have attempted to provide human parseable explanations justifying why a model performed a specific classification. Although many of these toolkits are available for use, it i…
Deep Reinforcement Learning (RL) has demonstrated to be useful for a wide variety of robotics applications. To address sample efficiency and safety during training, it is common to train Deep RL policies in a simulator and then deploy to the real world, a process called Sim2Real transfer. For roboti