Text-Driven Affordance Learning from Egocentric Vision: Predicting Contact Points and Manipulation Trajectories for Diverse Object Interactions

Core Concepts

This paper introduces a text-driven affordance learning approach that aims to learn contact points and manipulation trajectories from an egocentric view following textual instructions. The key idea is to employ textual input to cover a wide range of affordances for diverse objects and actions, including both hand-object and tool-object interactions.

Abstract

The paper presents a text-driven affordance learning approach that learns contact points and manipulation trajectories from egocentric vision. The key contributions are:

The authors introduce a text-driven affordance learning task that targets various affordances for a wide range of objects and actions, covering both hand-object and tool-object interactions.

To avoid costly manual annotations, the authors propose an automated approach to construct a large-scale pseudo-dataset, TextAFF80K, by leveraging egocentric video datasets like Ego4D and Epic-Kitchens.

The authors extend existing referring expression comprehension models, CLIPSeg and MDETR, to predict both contact points and trajectories. Experimental results show that the models trained on TextAFF80K robustly handle multiple affordances, particularly in tool-object interactions.

The authors find that considering both linear and rotational movements in the trajectory estimation contributes to representing complex manipulation trajectories.

Detailed analysis reveals that while conventional models perform better on hand-object interactions involving simple linear movements, the proposed text-driven models excel at capturing affordances in more complex tool-object interactions.

Stats

"To deploy collaborative robots in household and office environments, they should understand how to handle objects to perform human instructions effectively."
"Affordance, originally proposed by Gibson [1], is a key concept for understanding how to interact with objects. In computer vision (CV) and robotics, an affordance is often represented as contact points and manipulation trajectories [2], [3]."
"Previous studies have focused on learning affordances with pre-defined objects and actions, limiting robots' applicability in real-world scenarios because objects and actions in user instructions are diverse and it is infeasible to pre-define them."

Quotes

"The key idea of our approach is employing textual instruction, targeting various affordances for a wide range of objects. This approach covers both hand-object and tool-object interactions."
"To avoid manual annotations that are costly and time-consuming, we propose an automated approach that leverages homography and off-the-shelf tools, a hand-object detector and a dense points tracker, to construct a large-scale dataset from egocentric videos."
"Our experimental results demonstrate two insights. Firstly, models trained on our dataset robustly handle multiple affordances and show superior performance, particularly in tool-object interaction. Secondly, considering both linear and rotational movements in trajectory contributes to represent complex manipulation trajectories."

Key Insights Distilled From

Text-driven Affordance Learning from Egocentric Vision

by Tomoya Yoshi... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02523.pdf

Deeper Inquiries

How can the proposed text-driven affordance learning approach be extended to 3D environments and integrated with robotic systems to address real-world challenges?

The text-driven affordance learning approach can be extended to 3D environments by incorporating depth information from sensors like LiDAR or depth cameras. By integrating depth data with textual instructions, robots can better understand spatial relationships and interactions in three dimensions. This enhanced understanding can enable robots to navigate complex environments, manipulate objects more effectively, and interact with the surroundings in a more human-like manner. Additionally, the integration of 3D affordance learning can improve object recognition, grasp planning, and task execution in real-world scenarios. By training models on a combination of textual instructions and 3D data, robots can learn to perform a wider range of tasks with greater accuracy and efficiency.

What other types of textual information, beyond action descriptions, could be leveraged to further improve the learning of diverse affordances?

In addition to action descriptions, other types of textual information that could be leveraged to enhance the learning of diverse affordances include contextual cues, spatial relationships, and object properties. Contextual cues such as location descriptions, temporal references, and environmental conditions can provide valuable information for understanding how objects are used in different situations. Spatial relationships, such as proximity, orientation, and relative positions, can help robots infer how objects interact with each other and with the environment. Object properties like material, shape, and size can influence the affordances of objects and guide robots in selecting appropriate actions. By incorporating these additional types of textual information into the learning process, robots can gain a more comprehensive understanding of affordances and improve their ability to interact with objects in various contexts.

How can the automated dataset construction pipeline be enhanced to capture more nuanced interactions and handle a broader range of object and action types?

The automated dataset construction pipeline can be enhanced in several ways to capture more nuanced interactions and handle a broader range of object and action types. One approach is to incorporate multi-modal data sources, such as audio and haptic feedback, to provide additional context and sensory information for the learning process. By integrating data from multiple modalities, robots can learn from a more comprehensive set of inputs and improve their understanding of complex interactions. Additionally, the pipeline can be enhanced with active learning techniques that prioritize the annotation of data points that are most informative or challenging for the model. This targeted annotation strategy can help capture diverse interactions more efficiently and effectively. Furthermore, leveraging transfer learning and domain adaptation techniques can enable the pipeline to generalize across different object and action types, allowing robots to learn from a wider range of scenarios and tasks. By continuously refining and expanding the dataset construction pipeline with these enhancements, robots can improve their affordance learning capabilities and adapt to real-world challenges more effectively.