Offline reinforcement learning (RL) defines the task of learning from a fixed batch of data.
Due to errors in value estimation from out-of-distribution actions, most offline RL algorithms …

A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization
from existing datasets followed by fast online fine-tuning with limited interaction. However …

Reinforcement learning is a learning paradigm for solving sequential decision-making
problems. Recent years have witnessed remarkable progress in reinforcement learning …

With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …

Offline reinforcement learning (RL) defines the task of learning from a static logged dataset
without continually interacting with the environment. The distribution shift between the …

While significant progress has been made on understanding hand-object interactions in
computer vision, it is still very challenging for robots to perform complex dexterous …

We identify two issues with the family of algorithms based on the Adversarial Imitation
Learning framework. The first problem is implicit bias present in the reward functions used in …

Object manipulation from 3D visual inputs poses many challenges on building generalizable
perception and policy models. However, 3D assets in existing benchmarks mostly lack the …

Reinforcement learning from expert demonstrations (RLED) is the intersection of imitation
learning with reinforcement learning that seeks to take advantage of these two learning …

Unmanned aerial vehicles (UAVs) have the potential in delivering Internet-of-Things (IoT)
services from a great height, creating an airborne domain of the IoT. In this article, we …