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Found 14 papers, 3 papers with code
Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström method
In this paper, we study how the Koopman operator framework can be combined with kernel methods to effectively control nonlinear dynamical systems.
Towards Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot
The advent of tactile sensors in robotics has sparked many ideas on how robots can leverage direct contact measurements of their environment interactions to improve manipulation tasks.
Benchmarking the Sim-to-Real Gap in Cloth Manipulation
The dataset is collected by performing a dynamic as well as a quasi-static cloth manipulation task involving contact with a rigid table.
Deformable Surface Reconstruction via Riemannian Metric Preservation
Estimating the pose of an object from a monocular image is an inverse problem fundamental in computer vision.
The dGLI Cloth Coordinates: A Topological Representation for Semantic Classification of Cloth States
Robotic manipulation of cloth is a highly complex task because of its infinite-dimensional shape-state space that makes cloth state estimation very difficult.
Controlled Gaussian Process Dynamical Models with Application to Robotic Cloth Manipulation
A CGPDM is constituted by a low-dimensional latent space, with an associated dynamics where external control variables can act and a mapping to the observation space.
Online Action Recognition
It consists in recognizing, in an open world, the planning action that best explains a partially observable state transition from a knowledge library of first-order STRIPS actions, which is initially empty.
Encoding cloth manipulations using a graph of states and transitions
Cloth manipulation is very relevant for domestic robotic tasks, but it presents many challenges due to the complexity of representing, recognizing and predicting the behaviour of cloth under manipulation.
Gaussian-Process-based Robot Learning from Demonstration
Endowed with higher levels of autonomy, robots are required to perform increasingly complex manipulation tasks.
Robotics
STRIPS Action Discovery
This knowledge is usually handcrafted and is hard to keep updated, even for system experts.
Dynamic Cloth Manipulation with Deep Reinforcement Learning
Finally, we compare different combinations of control policy encodings, demonstrations, and sparse reward learning techniques, and show that our proposed approach can learn dynamic cloth manipulation in an efficient way, i. e., using a reduced observation space, a few demonstrations, and a sparse reward.
Robotics
Robust and Adaptive Door Operation with a Mobile Robot
In addition, we propose a versatile Bayesian framework that endows the robot with the ability to infer the door kinematic model from observations of its motion and learn from previous experiences or human demonstrations.
Robotics
Exploiting Single-Cycle Symmetries in Continuous Constraint Problems
Symmetries in discrete constraint satisfaction problems have been explored and exploited in the last years, but symmetries in continuous constraint problems have not received the same attention.
A Joint Model for 2D and 3D Pose Estimation from a Single Image
We introduce a novel approach to automatically recover 3D human pose from a single image.
Ranked #25 on
3D Human Pose Estimation
on HumanEva-I
