| 1. |
- Antanas, Laura, et al.
(author)
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Semantic and geometric reasoning for robotic grasping : a probabilistic logic approach
- 2019
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In: Autonomous Robots. - : Springer. - 0929-5593 .- 1573-7527. ; 43:6, s. 1393-1418
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Journal article (peer-reviewed)abstract
- While any grasp must satisfy the grasping stability criteria, good grasps depend on the specific manipulation scenario: the object, its properties and functionalities, as well as the task and grasp constraints. We propose a probabilistic logic approach for robot grasping, which improves grasping capabilities by leveraging semantic object parts. It provides the robot with semantic reasoning skills about the most likely object part to be grasped, given the task constraints and object properties, while also dealing with the uncertainty of visual perception and grasp planning. The probabilistic logic framework is task-dependent. It semantically reasons about pre-grasp configurations with respect to the intended task and employs object-task affordances and object/task ontologies to encode rules that generalize over similar object parts and object/task categories. The use of probabilistic logic for task-dependent grasping contrasts with current approaches that usually learn direct mappings from visual perceptions to task-dependent grasping points. The logic-based module receives data from a low-level module that extracts semantic objects parts, and sends information to the low-level grasp planner. These three modules define our probabilistic logic framework, which is able to perform robotic grasping in realistic kitchen-related scenarios.
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| 2. |
- Moldovan, Bogdan, et al.
(author)
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Relational affordances for multiple-object manipulation
- 2018
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In: Autonomous Robots. - : Springer. - 0929-5593 .- 1573-7527. ; 42:1, s. 19-44
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Journal article (peer-reviewed)abstract
- The concept of affordances has been used in robotics to model action opportunities of a robot and as a basis for making decisions involving objects. Affordances capture the interdependencies between the objects and their properties, the executed actions on those objects, and the effects of those respective actions. However, existing affordance models cannot cope with multiple objects that may interact during action execution. Our approach is unique in that possesses the following four characteristics. First, our model employs recent advances in probabilistic programming to learn affordance models that take into account (spatial) relations between different objects, such as relative distances. Two-object interaction models are first learned from the robot interacting with the world in a behavioural exploration stage, and are then employed in worlds with an arbitrary number of objects. The model thus generalizes over both the number of and the particular objects used in the exploration stage, and it also effectively deals with uncertainty. Secondly, rather than using a (discrete) action repertoire, the actions are parametrised according to the motor capabilities of the robot, which allows to model and achieve goals at several levels of complexity. It also supports a two-arm parametrised action. Thirdly, the relational affordance model represents the state of the world using both discrete (action and object features) and continuous (effects) random variables. The effects follow a multivariate Gaussian distribution with the correlated discrete variables (actions and object properties). Fourthly, the learned model can be employed on planning for high-level goals that closely correspond to goals formulated in natural language. The goals are specified by means of (spatial) relations between the objects. The model is evaluated in real experiments using an iCub robot given a series of such planning goals of increasing difficulty.
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| 3. |
- Moldovan, Bogdan, et al.
(author)
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Statistical Relational Learning of Object Affordances for Robotic Manipulation
- 2014
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In: Latest Advances in Inductive Logic Programming. - London : Imperial College Press. - 9781783265084 - 9781783265107 ; , s. 95-103
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Book chapter (peer-reviewed)abstract
- We present initial results of an application of statistical relational learning using ProbLog to a robotic manipulation task modeled using affordances. Affordances encompass the action possibilities on an object, so previous works have presented models for just one object. However, in scenarios where there are multiple objects that interact, it is very useful to consider the advantages of the statistical relational learning.
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