Robots operating in human-centered environments should have the ability to
understand how objects function: what can be done with each object, where this
interaction may occur, and how the object is used to achieve a goal. To this
end, we propose a novel approach that extracts a self-supervised visual
affordance model from human teleoperated play data and leverages it to enable
efficient policy learning and motion planning. We combine model-based planning
with model-free deep reinforcement learning (RL) to learn policies that favor
the same object regions favored by people, while requiring minimal robot
interactions with the environment. We evaluate our algorithm, Visual
Affordance-guided Policy Optimization (VAPO), with both diverse simulation
manipulation tasks and real world robot tidy-up experiments to demonstrate the
effectiveness of our affordance-guided policies. We find that our policies
train 4x faster than the baselines and generalize better to novel objects
because our visual affordance model can anticipate their affordance regions.

提出一种从人类遥操作的游戏数据中提取自我监督的视觉管用模型，并将其与基于模型的策略学习和基于模型的强化学习相结合，以实现有效的策略学习和运动规划，从而实现在人类环境中运作的机器人的高效操作。