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08/01/2023
DMFC-GraspNet: Differentiable Multi-Fingered Robotic Grasp Generation in Cluttered Scenes
Robotic grasping is a fundamental skill required for object manipulation...
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07/01/2023
SyMFM6D: Symmetry-aware Multi-directional Fusion for Multi-View 6D Object Pose Estimation
Detecting objects and estimating their 6D poses is essential for automat...
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06/22/2023
MP3: Movement Primitive-Based (Re-)Planning Policy
We introduce a novel deep reinforcement learning (RL) approach called Mo...
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06/21/2023
Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift
Bayesian deep learning (BDL) is a promising approach to achieve well-cal...
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04/11/2023
Curriculum-Based Imitation of Versatile Skills
Learning skills by imitation is a promising concept for the intuitive te...
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04/03/2023
Swarm Reinforcement Learning For Adaptive Mesh Refinement
Adaptive Mesh Refinement (AMR) is crucial for mesh-based simulations, as...
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03/27/2023
Information Maximizing Curriculum: A Curriculum-Based Approach for Training Mixtures of Experts
Mixtures of Experts (MoE) are known for their ability to learn complex c...
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02/23/2023
Grounding Graph Network Simulators using Physical Sensor Observations
Physical simulations that accurately model reality are crucial for many ...
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02/19/2023
LapGym – An Open Source Framework for Reinforcement Learning in Robot-Assisted Laparoscopic Surgery
Recent advances in reinforcement learning (RL) have increased the promis...
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02/10/2023
Reinforcement Learning from Multiple Sensors via Joint Representations
In many scenarios, observations from more than one sensor modality are a...
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10/18/2022
Deep Black-Box Reinforcement Learning with Movement Primitives
-based reinforcement learning (ERL) algorithms treat reinforcement learn...
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10/17/2022
On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning
Improved state space models, such as Recurrent State Space Models (RSSMs...
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10/17/2022
Inferring Versatile Behavior from Demonstrations by Matching Geometric Descriptors
Humans intuitively solve tasks in versatile ways, varying their behavior...
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10/04/2022
ProDMPs: A Unified Perspective on Dynamic and Probabilistic Movement Primitives
Movement Primitives (MPs) are a well-known concept to represent and gene...
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09/23/2022
A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models
Variational inference with Gaussian mixture models (GMMs) enables learni...
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09/22/2022
FusionVAE: A Deep Hierarchical Variational Autoencoder for RGB Image Fusion
Sensor fusion can significantly improve the performance of many computer...
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08/01/2022
MV6D: Multi-View 6D Pose Estimation on RGB-D Frames Using a Deep Point-wise Voting Network
Estimating 6D poses of objects is an essential computer vision task. How...
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07/31/2022
Robot Policy Learning from Demonstration Using Advantage Weighting and Early Termination
Learning robotic tasks in the real world is still highly challenging and...
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06/29/2022
Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios
Recurrent State-space models (RSSMs) are highly expressive models for le...
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05/27/2022
End-to-End Learning of Hybrid Inverse Dynamics Models for Precise and Compliant Impedance Control
It is well-known that inverse dynamics models can improve tracking perfo...
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05/24/2022
Regret-Aware Black-Box Optimization with Natural Gradients, Trust-Regions and Entropy Control
Most successful stochastic black-box optimizers, such as CMA-ES, use ran...
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03/15/2022
Reactive Motion Generation on Learned Riemannian Manifolds
In recent decades, advancements in motion learning have enabled robots t...
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03/09/2022
What Matters For Meta-Learning Vision Regression Tasks?
Meta-learning is widely used in few-shot classification and function reg...
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02/28/2022
Hierarchical Policy Learning for Mechanical Search
Retrieving objects from clutters is a complex task, which requires multi...
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12/08/2021
Specializing Versatile Skill Libraries using Local Mixture of Experts
A long-cherished vision in robotics is to equip robots with skills that ...
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11/29/2021
Human-machine Symbiosis: A Multivariate Perspective for Physically Coupled Human-machine Systems
The notion of symbiosis has been increasingly mentioned in research on p...
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11/16/2021
Switching Recurrent Kalman Networks
Forecasting driving behavior or other sensor measurements is an essentia...
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11/15/2021
Versatile Inverse Reinforcement Learning via Cumulative Rewards
Inverse Reinforcement Learning infers a reward function from expert demo...
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10/10/2021
Cooperative Assistance in Robotic Surgery through Multi-Agent Reinforcement Learning
Cognitive cooperative assistance in robot-assisted surgery holds the pot...
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08/06/2021
A Study on Dense and Sparse (Visual) Rewards in Robot Policy Learning
Deep Reinforcement Learning (DRL) is a promising approach for teaching r...
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07/08/2021
Navigate-and-Seek: a Robotics Framework for People Localization in Agricultural Environments
The agricultural domain offers a working environment where many human la...
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06/10/2021
Differentiable Robust LQR Layers
This paper proposes a differentiable robust LQR layer for reinforcement ...
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06/08/2021
Learning Riemannian Manifolds for Geodesic Motion Skills
For robots to work alongside humans and perform in unstructured environm...
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06/08/2021
Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty
While classic control theory offers state of the art solutions in many p...
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01/22/2021
Differentiable Trust Region Layers for Deep Reinforcement Learning
Trust region methods are a popular tool in reinforcement learning as the...
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10/20/2020
Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning
Estimating accurate forward and inverse dynamics models is a crucial com...
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08/10/2020
Imitation Learning for Autonomous Trajectory Learning of Robot Arms in Space
This work adds on to the on-going efforts to provide more autonomy to sp...
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08/08/2020
Non-Adversarial Imitation Learning and its Connections to Adversarial Methods
Many modern methods for imitation learning and inverse reinforcement lea...
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02/26/2020
Probabilistic approach to physical object disentangling
Physically disentangling entangled objects from each other is a problem ...
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01/23/2020
Expected Information Maximization: Using the I-Projection for Mixture Density Estimation
Modelling highly multi-modal data is a challenging problem in machine le...
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07/10/2019
Trust-Region Variational Inference with Gaussian Mixture Models
Many methods for machine learning rely on approximate inference from int...
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05/17/2019
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
In order to integrate uncertainty estimates into deep time-series modell...
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02/07/2019
Compatible Natural Gradient Policy Search
Trust-region methods have yielded state-of-the-art results in policy sea...
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11/16/2018
An Algorithmic Perspective on Imitation Learning
As robots and other intelligent agents move from simple environments and...
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08/31/2018
Adaptation and Robust Learning of Probabilistic Movement Primitives
Probabilistic representations of movement primitives open important new ...
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08/20/2018
Towards Fine Grained Network Flow Prediction
One main challenge for the design of networks is that traffic load is no...
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07/17/2018
Deep Reinforcement Learning for Swarm Systems
Recently, deep reinforcement learning (RL) methods have been applied suc...
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09/21/2017
Learning Complex Swarm Behaviors by Exploiting Local Communication Protocols with Deep Reinforcement Learning
Swarm systems constitute a challenging problem for reinforcement learnin...
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09/18/2017
Guided Deep Reinforcement Learning for Swarm Systems
In this paper, we investigate how to learn to control a group of coopera...
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11/10/2016
