7 accepted papers
This work addresses the challenge of reinforcement learning with reward functions that feature highly imbalanced components in terms of importance and scale. Reinforcement learning algorithms generally struggle to handle such imbalanced reward functions effectively. Consequently, they often converge
Imitation Learning (IL) is a promising paradigm for teaching robots to perform novel tasks using demonstrations. Most existing approaches for IL utilize neural networks (NN), however, these methods suffer from several well-known limitations: they 1) require large amounts of training data, 2) are har
Accurate kinodynamic models play a crucial role in many robotics applications such as off-road navigation and high-speed driving. Many state-of-the-art approaches for learning stochastic kinodynamic models, however, require precise measurements of robot states as labeled input/output examples, which…
Robots moving safely and in a socially compliant manner in dynamic human environments is an essential benchmark for long-term robot autonomy. However, it is not feasible to learn and benchmark social navigation behaviors entirely in the real world, as learning is data-intensive, and it is challengin…
Robot social navigation is influenced by human preferences and environment-specific scenarios such as elevators and doors, thus necessitating end-user adaptability. State-of-the-art approaches to social navigation fall into two categories: model-based social constraints and learning-based approaches…
Abstract: Action selection policies (ASPs), used to compose low-level robot skills into complex high-level tasks are commonly represented as neural networks (NNs) in the state of the art. Such a paradigm, while very effective, suffers from a few key problems: 1) NNs are opaque to the user and hence
Autonomous mobile robots that use multiple depth sensors to perceive their environments, rely on extrinsic calibration to combine the individual views from each sensor into a single coherent view of the surroundings. Such extrinsic calibration is tedious to perform manually, and requires that specif…