197 citations · 305 across the 11 of their papers we have counts for
13 papers
Deep Dexterous Grasping of Novel Objects from a Single View
Umit Rusen Aktas, Chao Zhao, Marek Kopicki +2
Dexterous grasping of a novel object given a single view is an open problem. This paper makes several contributions to its solution. First, we present a simulator for generating an…
Multisensory Learning Framework for Robot Drumming
A. Barsky, C. Zito, H. Mori +2
The hype about sensorimotor learning is currently reaching high fever, thanks to the latest advancement in deep learning. In this paper, we present an open-source framework for col…
Learning better generative models for dexterous, single-view grasping of novel objects
Marek Kopicki, Dominik Belter, Jeremy L. Wyatt
This paper concerns the problem of how to learn to grasp dexterously, so as to be able to then grasp novel objects seen only from a single view-point. Recently, progress has been m…
Generative grasp synthesis from demonstration using parametric mixtures
Ermano Arruda, Claudio Zito, Mohan Sridharan +2
We present a parametric formulation for learning generative models for grasp synthesis from a demonstration. We cast new light on this family of approaches, proposing a parametric…
Hypothesis-based Belief Planning for Dexterous Grasping
Claudio Zito, Valerio Ortenzi, Maxime Adjigble +3
Belief space planning is a viable alternative to formalise partially observable control problems and, in the recent years, its application to robot manipulation problems has grown.…
Uncertainty Averse Pushing with Model Predictive Path Integral Control
Ermano Arruda, Michael J Mathew, Marek Kopicki +3
Planning robust robot manipulation requires good forward models that enable robust plans to be found. This work shows how to achieve this using a forward model learned from robot d…
Active vision for dexterous grasping of novel objects
Ermano Arruda, Jeremy Wyatt, Marek Kopicki
How should a robot direct active vision so as to ensure reliable grasping? We answer this question for the case of dexterous grasping of unfamiliar objects. By dexterous grasping w…
Learning and Inference of Dexterous Grasps for Novel Objects with Underactuated Hands
Marek Kopicki, Carlos J. Rosales, Hamal Marino +2
Recent advances have been made in learning of grasps for fully actuated hands. A typical approach learns the target locations of finger links on the object. When a new object must…
Towards Deep Compositional Networks
Domen Tabernik, Matej Kristan, Jeremy L. Wyatt +1
Hierarchical feature learning based on convolutional neural networks (CNN) has recently shown significant potential in various computer vision tasks. While allowing high-quality di…
The STRANDS Project: Long-Term Autonomy in Everyday Environments
Nick Hawes, Chris Burbridge, Ferdian Jovan +30
Thanks to the efforts of the robotics and autonomous systems community, robots are becoming ever more capable. There is also an increasing demand from end-users for autonomous serv…
REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics
Mohan Sridharan, Michael Gelfond, Shiqi Zhang +1
This paper describes an architecture for robots that combines the complementary strengths of probabilistic graphical models and declarative programming to represent and reason with…
A Graph Theoretic Approach for Object Shape Representation in Compositional Hierarchies Using a Hybrid Generative-Descriptive Model
Umit Rusen Aktas, Mete Ozay, Ales Leonardis +1
A graph theoretic approach is proposed for object shape representation in a hierarchical compositional architecture called Compositional Hierarchy of Parts (CHOP). In the proposed…
KR: An Architecture for Knowledge Representation and Reasoning in Robotics
Shiqi Zhang, Mohan Sridharan, Michael Gelfond +1
This paper describes an architecture that combines the complementary strengths of declarative programming and probabilistic graphical models to enable robots to represent, reason w…