A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …

Robotic manipulation can be formulated as inducing a sequence of spatial displacements:
where the space being moved can encompass an object, part of an object, or end effector. In …

The uses of robots are changing from static environments in factories to encompass novel
concepts such as human–robot collaboration in unstructured settings. Preprogramming all …

Recently, a variety of new equivariant neural network model architectures have been
proposed that generalize better over rotational and reflectional symmetries than standard …

While it is generally acknowledged that force feedback is beneficial to robotic control,
applications of policy learning to robotic manipulation typically only leverage visual …

Enabling robots to solve multiple manipulation tasks has a wide range of industrial
applications. While learning-based approaches enjoy flexibility and generalizability, scaling …

In robotics, it is often not possible to learn useful policies using pure model-free
reinforcement learning without significant reward shaping or curriculum learning. As a …

Learning generalizable skills in robotic manipulation has long been challenging due to real-
world sized observation and action spaces. One method for addressing this problem is …

Abstract Model-free policy learning has been shown to be capable of learning manipulation
policies which can solve long-time horizon tasks using single-step manipulation primitives …

Sequential decision making under uncertainty problems often deal with partially observable
Markov decision processes (POMDPs). POMDPs mathematically capture making decisions …