Acquiring dynamics is an essential topic in robot learning, but up-to-date methods, such as
dynamics randomization, need to restart to check nominal parameters, generate simulation …

Acquiring dynamics is critical for robot learning and is fundamental to planning and control.
This paper concerns two fundamental questions: How can we learn a model that covers …

A deep learning-based approach can generalize model performance while reducing feature
design costs by learning end-to-end environment recognition and motion generation …

Online learning of a robot's inverse dynamics model for trajectory tracking necessitates an
interaction between the robot and its environment to collect training data. This is challenging …

We present RoboGen, a generative robotic agent that automatically learns diverse robotic
skills at scale via generative simulation. RoboGen leverages the latest advancements in …

Developing control policies in simulation is often more practical and safer than directly
running experiments in the real world. This applies to policies obtained from planning and …

Reinforcement learning has been demonstrated as a flexible and effective approach for
learning a range of continuous control tasks, such as those used by robots to manipulate …

We present a new neural network architecture, called NaturalNet, which uses a simplified
biological neuron model and consists of a set of nonlinear ordinary differential equations …

The robotic systems continuously interact with complex dynamical systems in the physical
world. Reliable predictions of spatiotemporal evolution of these dynamical systems, with …

Biological cortical networks are potentially fully recurrent networks without any distinct output
layer, where recognition may instead rely on the distribution of activity across its neurons …