Robotic manipulation challenges, such as gras** and object manipulation, have been
tackled successfully with the help of deep reinforcement learning systems. We give an …

Modeling complex physical dynamics is a fundamental task in science and engineering.
Traditional physics-based models are first-principled, explainable, and sample-efficient …

Building effective imitation learning methods that enable robots to learn from limited data
and still generalize across diverse real-world environments is a long-standing problem in …

Diffusion generative modeling has become a promising approach for learning robotic
manipulation tasks from stochastic human demonstrations. In this paper we present …

In planar grasp detection, the goal is to learn a function from an image of a scene onto a set
of feasible grasp poses in $\mathrm {SE}(2) $. In this paper, we recognize that the optimal …

Humans form mental images of 3D scenes to support counterfactual imagination, planning,
and motor control. Our abilities to predict the appearance and affordance of the scene from …

Embodied agents operate in a structured world, often solving tasks with spatial, temporal,
and permutation symmetries. Most algorithms for planning and model-based reinforcement …

Transporter Net is a recently proposed framework for pick and place that is able to learn
good manipulation policies from a very few expert demonstrations. A key reason why …

Incorporating inductive biases is a promising approach for tackling challenging robot
learning domains with sample-efficient solutions. This paper identifies partially observable …

End-to-end learning for visual robotic manipulation is known to suffer from sample
inefficiency, requiring large numbers of demonstrations. The spatial roto-translation …