Grasp Generalization Via Predictive Parts
نویسندگان
چکیده
Grasp densities are designed to be learned empirically. In principle, a robot attemps to grasp the object a large number of times using a wide variety of object-relative gripper poses. Each successful grasp constitutes a data point drawn from the underlying grasp density (1). In practice, for reasons of efficiency, attempted grasps should be chosen in an informed manner [1]. For resampling and inference, samples are turned into a continuous density by kernel density estimation.
منابع مشابه
How can I , robot , pick up that object with my hand ?
This paper describes a practical approach to the robot grasping problem. An approach that is composed of two different parts. First, a vision-based grasp synthesis system implemented on a humanoid robot able to compute a set of feasible grasps and to execute any of them. This grasping system takes into account gripper kinematics constraints and uses little computational effort. Second, a learni...
متن کاملPredicting others' actions via grasp and gaze: evidence for distinct brain networks.
During social interactions, how do we predict what other people are going to do next? One view is that we use our own motor experience to simulate and predict other people's actions. For example, when we see Sally look at a coffee cup or grasp a hammer, our own motor system provides a signal that anticipates her next action. Previous research has typically examined such gaze and grasp-based sim...
متن کاملBetter Generalization with Forecasts
Predictive methods are becoming increasingly popular for representing world knowledge in autonomous agents. A recently introduced predictive method that shows particular promise is the General Value Function (GVF), which is more flexible than previous predictive methods and can more readily capture regularities in the agent’s sensorimotor stream. The goal of the current paper is to investigate ...
متن کاملRecognizing the grasp intention from human demonstration
In human grasping, choices are made on the use of hand-parts even before a grasp is realized. The human associates these choices with end-functionality and is confident that the resulting grasp will be able to meet task requirements.We refer to these choices on the use of hand-parts underlying grasp formation as the grasp intention. Modeling the grasp intention offers a paradigm whereby decisio...
متن کاملLearning Reach-to-Grasp Motions From Human Demonstrations
R eaching over to grasp an item is arguably the most commonly used motor skill by humans. Even under sudden perturbations, humans seem to react rapidly and adapt their motion to guarantee success. Despite the apparent ease and frequency with which we use this ability, a complete understanding of the underlying mechanisms cannot be claimed. It is partly due to such incomplete knowledge that adap...
متن کامل