View publication

Applications such as autonomous navigation [1], human-robot interaction [2], game-playing robots [8], etc., use simulation to minimize the cost of testing in real world. Furthermore, some machine learning algorithms, like reinforcement learning, use simulation for training a model. To test reliably in simulation or deploy a model in the real world that is trained with simulated data, the simulator should be representative of the real environment. Usually, the simulator is based on manually designed rules and ignores the stochastic behavior of measurements. In particular, we would like to learn a model that captures uncertainties of the sensing algorithms (e.g. neural networks used to detect objects) in real world and add them in simulation. We model the distribution of residuals between the ground truth states of the objects and their perceived states by the sensing algorithm. This error distribution depends both on the current state of the object (e.g. distance from the sensor) and its past residuals. We assume the error distribution is conditionally Gaussian, and we use a deep neural neural network (DNN) to map the object states and past residuals to the distribution parameters (mean and variance). Our conditional model perturbs the dynamic objects’ states (position, velocities, orientations, and shape) and produces smoother trajectories which look similar to the real data.

Related readings and updates.

Robust Robotic Control from Pixels Using Contrastive Recurrent State-Space Models

Modeling the world can benefit robot learning by providing a rich training signal for shaping an agent's latent state space. However, learning world models in unconstrained environments over high-dimensional observation spaces such as images is challenging. One source of difficulty is the presence of irrelevant but hard-to-model background distractions, and unimportant visual details of task-relevant entities. We address this issue by learning a…
See paper details

Learning from Simulated and Unsupervised Images through Adversarial Training

With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. However, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions. To reduce this gap, we propose Simulated+Unsupervised (S+U) learning, where the task is to learn a model to improve the realism of a simulator's output…
See paper details