269 citations · 2k across the 61 of their papers we have counts for
13 papers · 2 filters
A Probabilistic Representation for Dynamic Movement Primitives
Franziska Meier, Stefan Schaal
Dynamic Movement Primitives have successfully been used to realize imitation learning, trial-and-error learning, reinforce- ment learning, movement recognition and segmentation and…
Probabilistic Articulated Real-Time Tracking for Robot Manipulation
Cristina Garcia Cifuentes, Jan Issac, Manuel Wüthrich +2
We propose a probabilistic filtering method which fuses joint measurements with depth images to yield a precise, real-time estimate of the end-effector pose in the camera frame. Th…
Learning Feedback Terms for Reactive Planning and Control
Akshara Rai, Giovanni Sutanto, Stefan Schaal +1
With the advancement of robotics, machine learning, and machine perception, increasingly more robots will enter human environments to assist with daily tasks. However, dynamically-…
Path Integral Guided Policy Search
Yevgen Chebotar, Mrinal Kalakrishnan, Ali Yahya +3
We present a policy search method for learning complex feedback control policies that map from high-dimensional sensory inputs to motor torques, for manipulation tasks with discont…
Stepping Stabilization Using a Combination of DCM Tracking and Step Adjustment
Majid Khadiv, Sebastien Kleff, Alexander Herzog +3
In this paper, a method for stabilizing biped robots stepping by a combination of Divergent Component of Motion (DCM) tracking and step adjustment is proposed. In this method, the…
DOOMED: Direct Online Optimization of Modeling Errors in Dynamics
Nathan Ratliff, Franziska Meier, Daniel Kappler +1
It has long been hoped that model-based control will improve tracking performance while maintaining or increasing compliance. This hope hinges on having or being able to estimate a…
A Convex Model of Momentum Dynamics for Multi-Contact Motion Generation
Brahayam Ponton, Alexander Herzog, Stefan Schaal +1
Linear models for control and motion generation of humanoid robots have received significant attention in the past years, not only due to their well known theoretical guarantees, b…
On the Fundamental Importance of Gauss-Newton in Motion Optimization
Nathan Ratliff, Marc Toussaint, Jeannette Bohg +1
Hessian information speeds convergence substantially in motion optimization. The better the Hessian approximation the better the convergence. But how good is a given approximation…
Automatic LQR Tuning Based on Gaussian Process Global Optimization
Alonso Marco, Philipp Hennig, Jeannette Bohg +2
This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller…
Structured contact force optimization for kino-dynamic motion generation
Alexander Herzog, Stefan Schaal, Ludovic Righetti
Optimal control approaches in combination with trajectory optimization have recently proven to be a promising control strategy for legged robots. Computationally efficient and robu…
Interactive Perception: Leveraging Action in Perception and Perception in Action
Jeannette Bohg, Karol Hausman, Bharath Sankaran +4
Recent approaches in robotics follow the insight that perception is facilitated by interaction with the environment. These approaches are subsumed under the term of Interactive Per…
Depth-Based Object Tracking Using a Robust Gaussian Filter
Jan Issac, Manuel Wüthrich, Cristina Garcia Cifuentes +3
We consider the problem of model-based 3D-tracking of objects given dense depth images as input. Two difficulties preclude the application of a standard Gaussian filter to this pro…
Inertial Sensor-Based Humanoid Joint State Estimation
Nicholas Rotella, Sean Mason, Stefan Schaal +1
This work presents methods for the determination of a humanoid robot's joint velocities and accelerations directly from link-mounted Inertial Measurement Units (IMUs) each containi…