Perceiving, Learning, and Exploiting Object Affordances for Autonomous Pile Manipulation

Katz, Dov; Venkatraman, Arun; Kazemi, Moslem; Bagnell, Drew; Stentz, Anthony

doi:10.15607/rss.2013.ix.039

Cited by 35 publications

(29 citation statements)

References 4 publications

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“…21(c)). Extensive experiments demonstrated the effectiveness and safety of our compliant grasping primitives in cluttered environments (please see [18,19] for more results and details).…”

Section: Object Release Experimentsmentioning

confidence: 99%

Human-inspired force compliant grasping primitives

et al. 2014

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We address the problem of grasping everyday objects that are small relative to an anthropomorphic hand, such as pens, screwdrivers, cellphones, and hammers from their natural poses on a support surface, e.g., a table top. In such conditions, state of the art grasp generation techniques fail to provide robust, achievable solutions due to either ignoring or trying to avoid contact with the support surface. In contrast, when people grasp small objects, they often make use of substantial contact with the support surface. In this paper we give results of human subjects grasping studies which show the extent and characteristics of environment contact under different task conditions. We develop a simple closed-loop hybrid grasping controller that mimics this interactive, contact-rich strategy by a position-force, pre-grasp and landing strategy for finger placement. The approach uses a compliant control of the hand during the grasp and release of objects in order to preserve safety. We conducted extensive robotic grasping experiments on a variety of small objects with similar shape and size. The results demonstrate that our approach is robust to localization uncertainties and applies to many everyday objects.

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“…21(c)). Extensive experiments demonstrated the effectiveness and safety of our compliant grasping primitives in cluttered environments (please see [18,19] for more results and details).…”

Section: Object Release Experimentsmentioning

confidence: 99%

Human-inspired force compliant grasping primitives

et al. 2014

Self Cite

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“…A↵ordances have been widely used in robotics for obtaining a functional understanding of the scene as well as enabling robots to interact and manipulate objects. These works range from predicting opportunities for interaction with an object by using only visual cues [40,9,2] to observing e↵ects of exploratory behaviors [31,36,30,10,13]. For instance, Sun et al [40] proposed a probabilistic graphical model that leverages visual object categorization for learning a↵ordances.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, Sun et al [40] proposed a probabilistic graphical model that leverages visual object categorization for learning a↵ordances. Katz et al [13] propose a framework for learning to manipulate objects in clutter by choosing robot actions based on object a↵ordances.…”

Section: Related Workmentioning

confidence: 99%

Anticipatory Planning for Human-Robot Teams

Koppula

Jain

Saxena

2015

Experimental Robotics

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Abstract. When robots work alongside humans for performing collaborative tasks, they need to be able to anticipate human's future actions and plan appropriate actions. The tasks we consider are performed in contextually-rich environments containing objects, and there is a large variation in the way humans perform these tasks. We use a graphical model to represent the state-space, where we model the humans through their low-level kinematics as well as their high-level intent, and model their interactions with the objects through physically-grounded object a↵ordances. This allows our model to anticipate a belief about possible future human actions, and we model the human's and robot's behavior through an MDP in this rich state-space. We further discuss that due to perception errors and the limitations of the model, the human may not take the optimal action and therefore we present robot's anticipatory planning with di↵erent behaviors of the human within the model's scope. In experiments on Cornell Activity Dataset, we show that our method performs better than various baselines for collaborative planning.

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“…Some other works consider human robot collaborations without anticipating human activities [1], focus on high-level actions [31], or consider object affordances for manipulation [16]. These works are orthogonal to ours.…”

Section: Related Workmentioning

confidence: 99%

Modeling High-Dimensional Humans for Activity Anticipation using Gaussian Process Latent CRFs

Jiang

Saxena

2014

Robotics: Science and Systems X

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Abstract-For robots, the ability to model human configurations and temporal dynamics is crucial for the task of anticipating future human activities, yet requires conflicting properties: On one hand, we need a detailed high-dimensional description of human configurations to reason about the physical plausibility of the prediction; on the other hand, we need a compact representation to be able to parsimoniously model the relations between the human and the environment.We therefore propose a new model, GP-LCRF, which admits both the high-dimensional and low-dimensional representation of humans. It assumes that the high-dimensional representation is generated from a latent variable corresponding to its lowdimensional representation using a Gaussian process. The generative process not only defines the mapping function between the high-and low-dimensional spaces, but also models a distribution of humans embedded as a potential function in GP-LCRF along with other potentials to jointly model the rich context among humans, objects and the activity. Through extensive experiments on activity anticipation, we show that our GP-LCRF consistently outperforms the state-of-the-art results and reduces the predicted human trajectory error by 11.6%.

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Perceiving, Learning, and Exploiting Object Affordances for Autonomous Pile Manipulation

Cited by 35 publications

References 4 publications

Human-inspired force compliant grasping primitives

Human-inspired force compliant grasping primitives

Anticipatory Planning for Human-Robot Teams

Modeling High-Dimensional Humans for Activity Anticipation using Gaussian Process Latent CRFs

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