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Found 12 papers, 0 papers with code
One-shot Learning for Autonomous Aerial Manipulation
This paper is concerned with learning transferable contact models for aerial manipulation tasks.
Direct Mutation and Crossover in Genetic Algorithms Applied to Reinforcement Learning Tasks
Neuroevolution has recently been shown to be quite competitive in reinforcement learning (RL) settings, and is able to alleviate some of the drawbacks of gradient-based approaches.
Dyna-T: Dyna-Q and Upper Confidence Bounds Applied to Trees
In reinforcement learning (RL) a planning agent has its own representation of the environment as a model.
Underwater Object Classification and Detection: first results and open challenges
First, we assessed if pretraining with the conventional ImageNet is beneficial when the object detector needs to be applied to environments that may be characterised by a different feature distribution.
Learning Transferable Push Manipulation Skills in Novel Contexts
We propose to learn a parametric internal model for push interactions that, similar for humans, enables a robot to predict the outcome of a physical interaction even in novel contexts.
Statistical Context-Dependent Units Boundary Correction for Corpus-based Unit-Selection Text-to-Speech
Unlike conventional techniques for speaker adaptation, which attempt to improve the accuracy of the segmentation using acoustic models that are more robust in the face of the speaker's characteristics, we aim to use only context dependent characteristics extrapolated with linguistic analysis techniques.
Robust and fast generation of top and side grasps for unknown objects
In this work, we present a geometry-based grasping algorithm that is capable of efficiently generating both top and side grasps for unknown objects, using a single view RGB-D camera, and of selecting the most promising one.
Generative grasp synthesis from demonstration using parametric mixtures
We present a parametric formulation for learning generative models for grasp synthesis from a demonstration.
Robotics
2D Linear Time-Variant Controller for Human's Intention Detection for Reach-to-Grasp Trajectories in Novel Scenes
Our approach also reduce the number of controllable dimensions for the user by providing only control on x- and y-axis, while orientation of the end-effector and the pose of its fingers are inferred by the system.
Let's Push Things Forward: A Survey on Robot Pushing
In doing so, we dedicate a separate section to deep learning approaches which have seen a recent upsurge in the literature.
Hypothesis-based Belief Planning for Dexterous Grasping
However, this planning approach was tried successfully only on simplified control problems.
