• Publications
  • Influence
Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors
TLDR
Dynamical movement primitives is presented, a line of research for modeling attractor behaviors of autonomous nonlinear dynamical systems with the help of statistical learning techniques, and its properties are evaluated in motor control and robotics. Expand
Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
TLDR
The approach achieves effective real-time control, can successfully grasp novel objects, and corrects mistakes by continuous servoing, and illustrates that data from different robots can be combined to learn more reliable and effective grasping. Expand
STOMP: Stochastic trajectory optimization for motion planning
TLDR
It is experimentally show that the stochastic nature of STOMP allows it to overcome local minima that gradient-based methods like CHOMP can get stuck in. Expand
Learning and generalization of motor skills by learning from demonstration
TLDR
A general approach for learning robotic motor skills from human demonstration is provided and how this framework extends to the control of gripper orientation and finger position and the feasibility of this approach is demonstrated. Expand
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
TLDR
QT-Opt is introduced, a scalable self-supervised vision-based reinforcement learning framework that can leverage over 580k real-world grasp attempts to train a deep neural network Q-function with over 1.2M parameters to perform closed-loop, real- world grasping that generalizes to 96% grasp success on unseen objects. Expand
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
TLDR
This work study how randomized simulated environments and domain adaptation methods can be extended to train a grasping system to grasp novel objects from raw monocular RGB images, including a novel extension of pixel-level domain adaptation that is term the GraspGAN. Expand
Biologically-inspired dynamical systems for movement generation: Automatic real-time goal adaptation and obstacle avoidance
TLDR
An improved modification of the original dynamic movement primitive (DMP) framework is presented, which can generalize movements to new targets without singularities and large accelerations and represent a movement in 3D task space without depending on the choice of coordinate system. Expand
Movement reproduction and obstacle avoidance with dynamic movement primitives and potential fields
TLDR
A general framework for movement generation and mid-flight adaptation to obstacles is presented and obstacle avoidance is included by adding to the equations of motion a repellent force - a gradient of a potential field centered around the obstacle. Expand
Learning objective functions for manipulation
TLDR
An approach to learning objective functions for robotic manipulation based on inverse reinforcement learning that can deal with high-dimensional continuous state-action spaces, and only requires local optimality of demonstrated trajectories is presented. Expand
Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection
TLDR
A large convolutional neural network is trained to predict the probability that task-space motion of the gripper will result in successful grasps, using only monocular camera images and independently of camera calibration or the current robot pose. Expand
...
1
2
3
4
5
...