We present a deep-dive into a real-world robotic learning system that, i...
We consider how to most efficiently leverage teleoperator time to collec...
Sim-to-real transfer is a powerful paradigm for robotic reinforcement
le...
Reinforcement learning (RL) algorithms hold the promise of enabling
auto...
Reinforcement learning provides a general framework for flexible decisio...
Reinforcement learning has been applied to a wide variety of robotics
pr...
The success of reinforcement learning for real world robotics has been, ...
While robot learning has demonstrated promising results for enabling rob...
The combination of deep neural network models and reinforcement learning...
Reinforcement learning and planning methods require an objective or rewa...
The design of a reward function often poses a major practical challenge ...
Standard model-free deep reinforcement learning (RL) algorithms sample a...
We tackle the problem of learning robotic sensorimotor control policies ...
We introduce the task of Visual Dialog, which requires an AI agent to ho...
Advanced Driver Assistance Systems (ADAS) have made driving safer over t...
Anticipating the future actions of a human is a widely studied problem i...