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Found 13 papers, 3 papers with code
Kinematically-Decoupled Impedance Control for Fast Object Visual Servoing and Grasping on Quadruped Manipulators
We demonstrate the performance and robustness of the proposed approach with various experiments on our 140 kg HyQReal quadruped robot equipped with a 7-DoF manipulator arm.
Intuitive Tasks Planning Using Visuo-Tactile Perception for Human Robot Cooperation
Designing robotic tasks for co-manipulation necessitates to exploit not only proprioceptive but also exteroceptive information for improved safety and autonomy.
Reproducible Pruning System on Dynamic Natural Plants for Field Agricultural Robots
The Natural Admittance Controller (NAC) is applied to deal with the dynamics of vines.
Robotics Systems and Control Systems and Control
A Linearly Constrained Nonparametric Framework for Imitation Learning
Several examples including simulated writing and locomotion tasks are presented to show the effectiveness of our framework.
Hierarchical Reinforcement Learning for Concurrent Discovery of Compound and Composable Policies
Moreover, such composition of individual policies is usually performed sequentially, which is not suitable for tasks that require to perform the subtasks concurrently.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Simultaneous Contact, Gait and Motion Planning for Robust Multi-Legged Locomotion via Mixed-Integer Convex Optimization
In this paper, we propose a mixed-integer convex formulation to plan simultaneously contact locations, gait transitions and motion, in a computationally efficient fashion.
On-line and on-board planning and perception for quadrupedal locomotion
We use a lattice representation together with a set of defined body movement primitives for computing a body action plan.
V2CNet: A Deep Learning Framework to Translate Videos to Commands for Robotic Manipulation
We propose V2CNet, a new deep learning framework to automatically translate the demonstration videos to commands that can be directly used in robotic applications.
Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives
This rich set of information is used in combination with optimal controller fusion to learn actions from data, with two main advantages: i) robots become safe when uncertain about their actions and ii) they are able to leverage partial demonstrations, given as elementary sub-tasks, to optimally perform a higher level, more complex task.
Object Captioning and Retrieval with Natural Language
The key idea of our approach is the use of object descriptions to provide the detailed understanding of an object.
Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints
When learning skills from demonstrations, one is often required to think in advance about the appropriate task representation (usually in either operational or configuration space).
Translating Videos to Commands for Robotic Manipulation with Deep Recurrent Neural Networks
We present a new method to translate videos to commands for robotic manipulation using Deep Recurrent Neural Networks (RNN).
Real-Time 6DOF Pose Relocalization for Event Cameras with Stacked Spatial LSTM Networks
Our method first creates the event image from a list of events that occurs in a very short time interval, then a Stacked Spatial LSTM Network (SP-LSTM) is used to learn the camera pose.
