Building a robot that can understand and learn to interact by watching humans
has inspired several vision problems. However, despite some successful results
on static datasets, it remains unclear how current models can be used on a
robot directly. In this paper, we aim to bridge this gap by leveraging videos
of human interactions in an environment centric manner. Utilizing internet
videos of human behavior, we train a visual affordance model that estimates
where and how in the scene a human is likely to interact. The structure of
these behavioral affordances directly enables the robot to perform many complex
tasks. We show how to seamlessly integrate our affordance model with four robot
learning paradigms including offline imitation learning, exploration,
goal-conditioned learning, and action parameterization for reinforcement
learning. We show the efficacy of our approach, which we call VRB, across 4
real world environments, over 10 different tasks, and 2 robotic platforms
operating in the wild. Results, visualizations and videos at
this https URL

本论文探讨了如何通过利用互联网视频中的人类行为，训练一个可视化驱动的视觉能力模型，以此实现机器人在现实环境中的复杂任务执行。我们将该模型与四个机器人学习范式无缝连接，并在 4 个现实世界环境、超过 10 种不同任务和 2 个机器人平台中展示了其效力。

以人类视频中的操作性作为机器人的通用表现形式

Affordances from Human Videos as a Versatile Representation for Robotics

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。

从游戏中学习可操作性以实现高效策略学习

Affordance Learning from Play for Sample-Efficient Policy Learning

Dexterous robotic hands are appealing for their agility and human-like
morphology, yet their high degree of freedom makes learning to manipulate
challenging. We introduce an approach for learning dexterous grasping. Our key
idea is to embed an object-centric visual affordance model within a deep
reinforcement learning loop to learn grasping policies that favor the same
object regions favored by people. Unlike traditional approaches that learn from
human demonstration trajectories (e.g., hand joint sequences captured with a
glove), the proposed prior is object-centric and image-based, allowing the
agent to anticipate useful affordance regions for objects unseen during policy
learning. We demonstrate our idea with a 30-DoF five-fingered robotic hand
simulator on 40 objects from two datasets, where it successfully and
efficiently learns policies for stable functional grasps. Our affordance-guided
policies are significantly more effective, generalize better to novel objects,
train 3 X faster than the baselines, and are more robust to noisy sensor
readings and actuation. Our work offers a step towards manipulation agents that
learn by watching how people use objects, without requiring state and action
information about the human body. Project website:
this http URL

本研究介绍了一种在深度强化学习循环中嵌入面向对象视觉助力模型的方法，以学习优先选择与人类喜欢的对象区域相同的抓握策略，实现对物体的灵活抓握能力。通过 40 个物体的实验，表明该方法可以显著提高抓握策略效能，泛化能力较强，比普通基线方法的训练速度更快，且更能适应噪声传感器。