Search Results for author: Stefan Schaal

Found 32 papers, 9 papers with code

Detection and Physical Interaction with Deformable Linear Objects

no code implementations17 May 2022 Azarakhsh Keipour, Mohammadreza Mousaei, Maryam Bandari, Stefan Schaal, Sebastian Scherer

On the other hand, while physical interaction with these objects has been done with ground manipulators, there have not been any studies on physical interaction and manipulation of the deformable linear object with aerial robots.

Efficient Spatial Representation and Routing of Deformable One-Dimensional Objects for Manipulation

no code implementations13 Feb 2022 Azarakhsh Keipour, Maryam Bandari, Stefan Schaal

On the other hand, algorithms that entirely separate the spatial representation of the deformable object from the routing and manipulation, often using a representation approach independent of planning, result in slow planning in high dimensional space.

You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration

1 code implementation30 Jan 2022 Bowen Wen, Wenzhao Lian, Kostas Bekris, Stefan Schaal

The canonical object representation is learned solely in simulation and then used to parse a category-level, task trajectory from a single demonstration video.


Deformable One-Dimensional Object Detection for Routing and Manipulation

no code implementations18 Jan 2022 Azarakhsh Keipour, Maryam Bandari, Stefan Schaal

To the best of our knowledge, the topic of detection methods that can extract those initial conditions in non-trivial situations has hardly been addressed.

Object Detection

Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation

no code implementations ICLR 2022 Todor Davchev, Oleg Sushkov, Jean-Baptiste Regli, Stefan Schaal, Yusuf Aytar, Markus Wulfmeier, Jon Scholz

In this work, we extend hindsight relabelling mechanisms to guide exploration along task-specific distributions implied by a small set of successful demonstrations.

Continuous Control

CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation

1 code implementation19 Sep 2021 Bowen Wen, Wenzhao Lian, Kostas Bekris, Stefan Schaal

This work proposes a framework to learn task-relevant grasping for industrial objects without the need of time-consuming real-world data collection or manual annotation.

Domain Generalization Grasp Contact Prediction +4

Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study

no code implementations21 Mar 2021 Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute, Wenzhao Lian, Chang Su, Mel Vecerik, Ning Ye, Stefan Schaal, Jon Scholz

In this paper we define criteria for industry-oriented DRL, and perform a thorough comparison according to these criteria of one family of learning approaches, DRL from demonstration, against a professional industrial integrator on the recently established NIST assembly benchmark.

Benchmarking Off-The-Shelf Solutions to Robotic Assembly Tasks

no code implementations8 Mar 2021 Wenzhao Lian, Tim Kelch, Dirk Holz, Adam Norton, Stefan Schaal

In recent years, many learning based approaches have been studied to realize robotic manipulation and assembly tasks, often including vision and force/tactile feedback.

Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation

no code implementations18 Aug 2020 Todor Davchev, Kevin Sebastian Luck, Michael Burke, Franziska Meier, Stefan Schaal, Subramanian Ramamoorthy

Dynamic Movement Primitives (DMP) are a popular way of extracting such policies through behaviour cloning (BC) but can struggle in the context of insertion.

Behavioural cloning

A New Age of Computing and the Brain

no code implementations27 Apr 2020 Polina Golland, Jack Gallant, Greg Hager, Hanspeter Pfister, Christos Papadimitriou, Stefan Schaal, Joshua T. Vogelstein

In December 2014, a two-day workshop supported by the Computing Community Consortium (CCC) and the National Science Foundation's Computer and Information Science and Engineering Directorate (NSF CISE) was convened in Washington, DC, with the goal of bringing together computer scientists and brain researchers to explore these new opportunities and connections, and develop a new, modern dialogue between the two research communities.

Simulator Predictive Control: Using Learned Task Representations and MPC for Zero-Shot Generalization and Sequencing

1 code implementation4 Oct 2018 Zhanpeng He, Ryan Julian, Eric Heiden, Hejia Zhang, Stefan Schaal, Joseph J. Lim, Gaurav Sukhatme, Karol Hausman

We complete unseen tasks by choosing new sequences of skill latents to control the robot using MPC, where our MPC model is composed of the pre-trained skill policy executed in the simulation environment, run in parallel with the real robot.

Scaling simulation-to-real transfer by learning composable robot skills

1 code implementation26 Sep 2018 Ryan Julian, Eric Heiden, Zhanpeng He, Hejia Zhang, Stefan Schaal, Joseph J. Lim, Gaurav Sukhatme, Karol Hausman

In particular, we first use simulation to jointly learn a policy for a set of low-level skills, and a "skill embedding" parameterization which can be used to compose them.

Probabilistic Recurrent State-Space Models

2 code implementations ICML 2018 Andreas Doerr, Christian Daniel, Martin Schiegg, Duy Nguyen-Tuong, Stefan Schaal, Marc Toussaint, Sebastian Trimpe

State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification.

Gaussian Processes Time Series +1

Combining Learned and Analytical Models for Predicting Action Effects from Sensory Data

1 code implementation11 Oct 2017 Alina Kloss, Stefan Schaal, Jeannette Bohg

In this work, we investigate the advantages and limitations of neural network based learning approaches for predicting the effects of actions based on sensory input and show how analytical and learned models can be combined to leverage the best of both worlds.

Online Learning of a Memory for Learning Rates

1 code implementation20 Sep 2017 Franziska Meier, Daniel Kappler, Stefan Schaal

The promise of learning to learn for robotics rests on the hope that by extracting some information about the learning process itself we can speed up subsequent similar learning tasks.

Meta-Learning online learning

On the Design of LQR Kernels for Efficient Controller Learning

no code implementations20 Sep 2017 Alonso Marco, Philipp Hennig, Stefan Schaal, Sebastian Trimpe

Finding optimal feedback controllers for nonlinear dynamic systems from data is hard.

Time-Contrastive Networks: Self-Supervised Learning from Video

4 code implementations23 Apr 2017 Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, Sergey Levine

While representations are learned from an unlabeled collection of task-related videos, robot behaviors such as pouring are learned by watching a single 3rd-person demonstration by a human.

Metric Learning reinforcement-learning +2

Path Integral Guided Policy Search

no code implementations3 Oct 2016 Yevgen Chebotar, Mrinal Kalakrishnan, Ali Yahya, Adrian Li, Stefan Schaal, Sergey Levine

We extend GPS in the following ways: (1) we propose the use of a model-free local optimizer based on path integral stochastic optimal control (PI2), which enables us to learn local policies for tasks with highly discontinuous contact dynamics; and (2) we enable GPS to train on a new set of task instances in every iteration by using on-policy sampling: this increases the diversity of the instances that the policy is trained on, and is crucial for achieving good generalization.

A Convex Model of Momentum Dynamics for Multi-Contact Motion Generation

no code implementations28 Jul 2016 Brahayam Ponton, Alexander Herzog, Stefan Schaal, Ludovic Righetti

This model is non-linear and non-convex; however, we find a relaxation of the problem that allows us to formulate it as a single convex quadratically-constrained quadratic program (QCQP) that can be very efficiently optimized.


Automatic LQR Tuning Based on Gaussian Process Global Optimization

no code implementations6 May 2016 Alonso Marco, Philipp Hennig, Jeannette Bohg, Stefan Schaal, Sebastian Trimpe

With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data.

Interactive Perception: Leveraging Action in Perception and Perception in Action

no code implementations13 Apr 2016 Jeannette Bohg, Karol Hausman, Bharath Sankaran, Oliver Brock, Danica Kragic, Stefan Schaal, Gaurav Sukhatme

Recent approaches in robotics follow the insight that perception is facilitated by interaction with the environment.


Depth-Based Object Tracking Using a Robust Gaussian Filter

1 code implementation19 Feb 2016 Jan Issac, Manuel Wüthrich, Cristina Garcia Cifuentes, Jeannette Bohg, Sebastian Trimpe, Stefan Schaal

To address this issue, we show how a recently published robustification method for Gaussian filters can be applied to the problem at hand.

Object Tracking Outlier Detection

Robust Gaussian Filtering using a Pseudo Measurement

no code implementations14 Sep 2015 Manuel Wüthrich, Cristina Garcia Cifuentes, Sebastian Trimpe, Franziska Meier, Jeannette Bohg, Jan Issac, Stefan Schaal

The contribution of this paper is to show that any Gaussian filter can be made compatible with fat-tailed sensor models by applying one simple change: Instead of filtering with the physical measurement, we propose to filter with a pseudo measurement obtained by applying a feature function to the physical measurement.

Incremental Local Gaussian Regression

no code implementations NeurIPS 2014 Franziska Meier, Philipp Hennig, Stefan Schaal

Locally weighted regression (LWR) was created as a nonparametric method that can approximate a wide range of functions, is computationally efficient, and can learn continually from very large amounts of incrementally collected data.

Local Gaussian Regression

no code implementations4 Feb 2014 Franziska Meier, Philipp Hennig, Stefan Schaal

Locally weighted regression was created as a nonparametric learning method that is computationally efficient, can learn from very large amounts of data and add data incrementally.

Bayesian Kernel Shaping for Learning Control

no code implementations NeurIPS 2008 Jo-Anne Ting, Mrinal Kalakrishnan, Sethu Vijayakumar, Stefan Schaal

In this paper, we focus on nonparametric regression and introduce a Bayesian formulation that, with the help of variational approximations, results in an EM-like algorithm for simultaneous estimation of regression and kernel parameters.

Gaussian Processes

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