Point Policy: Unifying Observations and Actions with Key Points for Robot Manipulation (2502.20391v1)

Abstract: Building robotic agents capable of operating across diverse environments and object types remains a significant challenge, often requiring extensive data collection. This is particularly restrictive in robotics, where each data point must be physically executed in the real world. Consequently, there is a critical need for alternative data sources for robotics and frameworks that enable learning from such data. In this work, we present Point Policy, a new method for learning robot policies exclusively from offline human demonstration videos and without any teleoperation data. Point Policy leverages state-of-the-art vision models and policy architectures to translate human hand poses into robot poses while capturing object states through semantically meaningful key points. This approach yields a morphology-agnostic representation that facilitates effective policy learning. Our experiments on 8 real-world tasks demonstrate an overall 75% absolute improvement over prior works when evaluated in identical settings as training. Further, Point Policy exhibits a 74% gain across tasks for novel object instances and is robust to significant background clutter. Videos of the robot are best viewed at https://point-policy.github.io/.

PointER: Unifying Observations and Actions with Key Points for Robot Manipulation

The paper "PointER: Unifying Observations and Actions with Key Points for Robot Manipulation" introduces an innovative method to enhance the learning of robot policies using human demonstration videos as the sole training data source. Given the traditional challenges in robotics, such as data scarcity due to the necessity of real-world execution, PointER aims to leverage human videos to develop generalizable robotic manipulation skills. The core of this approach lies in the translation of human demonstrations into a morphology-agnostic representation, significantly bridging the morphology gap between human and robot actions.

PointER utilizes state-of-the-art vision models to translate human hand dynamics into corresponding robot manipulations, identifying semantically meaningful key points on objects within the demonstration context. This transformation is achieved by capturing the human hand poses and the 3D spatial configuration of manipulated objects, which are then used to form a universal representation of tasks that robots can interpret and execute. This methodology stands out by not requiring any teleoperation data nor physical robot interactions during training, which marks a departure from the prevalent approaches that blend human and robotic datasets or rely heavily on real-world, expensive, and time-consuming robot interactions.

In experimental settings across eight diverse real-world tasks, PointER demonstrated an absolute performance improvement of 75% over prior methodologies when evaluated in identical configurations. Additionally, it achieved a 74% success rate in tasks involving novel object instances, highlighting its robustness across various task conditions, spatial generalizations, and amidst background clutter.

The implications of this research are multifaceted. Practically, PointER proposes a cost-effective and scalable pipeline for robot policy learning, potentially broadening the scope of automation in unstructured environments with varied object types without necessitating an extensive dataset of robot-centric demonstrations. Theoretically, it opens new research avenues in harnessing human video datasets for robotic applications, suggesting a paradigm shift from learning specific task actions to understanding and replicating human intentions in the robotic domain.

Future advancements might see PointER extended to real-time applications, where dynamic adjustments to unforeseen environmental changes can be further refined. Additionally, while PointER capitalizes on the advantages of current vision models, ongoing improvements in areas such as semantic correspondence and point tracking could further bolster its efficiency and adaptability.

In summary, PointER presents a compelling framework for redefining robot learning paradigms by effectively utilizing key point-based representations to unify perceptual and action spaces across human and robotic domains, offering promising strides toward seamless human-to-robot transfer of skills.