Exercising Affordances of Objects: A Part-Based Approach

Lakani, Safoura Rezapour; Rodríguez-Sánchez, Antonio Jose; Piater, Justus

doi:10.1109/lra.2018.2853639

Cited by 10 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Brawer et al, 2020; Coelho et al, 2001; Detry et al, 2010; Montesano et al, 2008). For instance, Lakani et al (2018) demonstrate how a robotic system can, through analogical reasoning, learn the object affordances of pouring coffee into different kinds of containers without having experience of that particular container.…”

Section: Related Workmentioning

confidence: 99%

Curiously exploring affordance spaces of a pouring task

2022

View full text Add to dashboard Cite

Human beings and other biological agents appear driven by curiosity to explore the affordances of their environments. Such exploration is its own rewardchildren have fun when playingbut it probably also serves the practical purpose of learning theories with which to predict outcomes of actions. Cognitive robots however have yet to match the performance of human beings at learning and reusing manipulation skills. In this paper, we implement a method that emulates the curiosity drive and uses it as a heuristic to guide (simulated) exploration of a particular taskpouring liquids. The result of this exploration is a collection of symbolic rules linking qualitative descriptions of object arrangements and the pouring action with qualitative descriptions of likely outcomes. The manner in which qualitative descriptions of object arrangements and actions are converted to numerical descriptions for the purpose of simulation parametrization is via probability distributions, which themselves are adjusted in the process of simulated exploration. This allows the grounding of the symbolic descriptions to attempt to adapt itself to the task. The resulting symbolic rules form a theory that, together with the probability distributions that ground it in numerical parametrizations, is intended to be used to predict qualitative outcomes or select manners of pouring towards achieving a goal.

show abstract

Section: Related Workmentioning

confidence: 99%

Curiously exploring affordance spaces of a pouring task

2022

View full text Add to dashboard Cite

show abstract

“…Instead of relying on low-level features, Aleotti et al [15] modeled semantic grasps with object parts obtained from topological shape decomposition and learned the parts that should be grasped at different stages of a task. Lakani et al [16] learned the co-occurrence frequency of manipulative affordances (affordances associated with the tasks) and executive affordances (affordances of the parts which need to be grasped). These last two works are closest in spirit to our approach, but we are reasoning over a wider variety of contextual information.…”

Section: A Semantic Graspingmentioning

confidence: 99%

“…grasping based on part affordances. This benchmark is adapted from [16], with the difference that our representation replaces executive affordances with contexts. This method learns the co-occurrence frequency of grasp affordances and contexts.…”

Section: • Frequency Table Of Affordances (Ft) Performs Semanticmentioning

confidence: 99%

CAGE: Context-Aware Grasping Engine

Liu

Daruna

Chernova

2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

Semantic grasping is the problem of selecting stable grasps that are functionally suitable for specific object manipulation tasks. In order for robots to effectively perform object manipulation, a broad sense of context, including object and task constraints, needs to be accounted for. We introduce the Context-Aware Grasping Engine, a neural network structure based on the Wide & Deep model, to learn suitable semantic grasps from data. We quantitatively validate our approach against three prior methods on a novel dataset consisting of 14,000 semantic grasps for 44 objects, 7 tasks, and 6 different object states. Our approach outperformed all baselines by statistically significant margins, producing new insights into the importance of balancing memorization and generalization of contexts for semantic grasping. We further demonstrate the effectiveness of our approach on robot experiments in which the presented model successfully achieved 31 of 32 grasps.

show abstract

“…Lakani et al [ 85 ] proposed an RGB-D part-based approach for task performance. The affordances are detected and associated with parts of the object.…”

Section: Physical-uncertain Objectsmentioning

confidence: 99%

Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review

Wang

Zhang

Zang

et al. 2020

Sensors

View full text Add to dashboard Cite

As there come to be more applications of intelligent robots, their task object is becoming more varied. However, it is still a challenge for a robot to handle unfamiliar objects. We review the recent work on the feature sensing and robotic grasping of objects with uncertain information. In particular, we focus on how the robot perceives the features of an object, so as to reduce the uncertainty of objects, and how the robot completes object grasping through the learning-based approach when the traditional approach fails. The uncertain information is classified into geometric information and physical information. Based on the type of uncertain information, the object is further classified into three categories, which are geometric-uncertain objects, physical-uncertain objects, and unknown objects. Furthermore, the approaches to the feature sensing and robotic grasping of these objects are presented based on the varied characteristics of each type of object. Finally, we summarize the reviewed approaches for uncertain objects and provide some interesting issues to be more investigated in the future. It is found that the object’s features, such as material and compactness, are difficult to be sensed, and the object grasping approach based on learning networks plays a more important role when the unknown degree of the task object increases.

show abstract

Exercising Affordances of Objects: A Part-Based Approach

Cited by 10 publications

References 18 publications

Curiously exploring affordance spaces of a pouring task

Curiously exploring affordance spaces of a pouring task

CAGE: Context-Aware Grasping Engine

Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review

Contact Info

Product

Resources

About