5 research outputs found
Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios
Recurrent State-space models (RSSMs) are highly expressive models for
learning patterns in time series data and system identification. However, these
models assume that the dynamics are fixed and unchanging, which is rarely the
case in real-world scenarios. Many control applications often exhibit tasks
with similar but not identical dynamics which can be modeled as a latent
variable. We introduce the Hidden Parameter Recurrent State Space Models
(HiP-RSSMs), a framework that parametrizes a family of related dynamical
systems with a low-dimensional set of latent factors. We present a simple and
effective way of learning and performing inference over this Gaussian graphical
model that avoids approximations like variational inference. We show that
HiP-RSSMs outperforms RSSMs and competing multi-task models on several
challenging robotic benchmarks both on real-world systems and simulations.Comment: Published at the International Conference on Learning
Representations, ICLR 202
Action conditional recurrent Kalman networks for forward and inverse dynamics learning
Estimating accurate forward and inverse dynamics models is a crucial component of model-based control for sophisticated robots such as robots driven by hydraulics, artificial muscles, or robots dealing with different contact situations. Analytic models to such processes are often unavailable or inaccurate due to complex hysteresis effects, unmodelled friction and stiction phenomena, and unknown effects during contact situations. A promising approach is to obtain spatio-temporal models in a data-driven way using recurrent neural networks, as they can overcome those issues. However, such models often do not meet accuracy demands sufficiently, degenerate in performance for the required high sampling frequencies and cannot provide uncertainty estimates. We adopt a recent probabilistic recurrent neural network architecture, called Recurrent Kalman Networks (RKNs), to model learning by conditioning its transition dynamics on the control actions. RKNs outperform standard recurrent networks such as LSTMs on many state estimation tasks. Inspired by Kalman filters, the RKN provides an elegant way to achieve action conditioning within its recurrent cell by leveraging additive interactions between the current latent state and the action variables. We present two architectures, one for forward model learning and one for inverse model learning. Both architectures significantly outperform existing model learning frameworks as well as analytical models in terms of prediction performance on a variety of real robot dynamics models
Zero-shot knowledge distillation in deep networks
Knowledge distillation deals with the problem of training a smaller model (Student) from a high capacity source model (Teacher) so as to retain most of its performance. Existing approaches use either the training data or meta-data extracted from it in order to train the Student. However, accessing the dataset on which the Teacher has been trained may not always be feasible if the dataset is very large or it poses privacy or safety concerns (e.g., bio-metric or medical data). Hence, in this paper, we propose a novel data-free method to train the Student from the Teacher. Without even using any meta-data, we synthesize the Data Impressions from the complex Teacher model and utilize these as surrogates for the original training data samples to transfer its learning to Student via knowledge distillation. We, therefore, dub our method "Zero-Shot Knowledge Distillation" and demonstrate that our framework results in competitive generalization performance as achieved by distillation using the actual training data samples on multiple benchmark datasets