
Convex Optimization with an Interpolationbased Projection and its Application to Deep Learning
Convex optimizers have known many applications as differentiable layers ...
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A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning
Probabilistic regression techniques in control and robotics applications...
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Differentiable Physics Models for Realworld Offline Modelbased Reinforcement Learning
A limitation of modelbased reinforcement learning (MBRL) is the exploit...
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Batch Reinforcement Learning with a Nonparametric OffPolicy Policy Gradient
Offpolicy Reinforcement Learning (RL) holds the promise of better data ...
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Contextual LatentMovements OffPolicy Optimization for Robotic Manipulation Skills
Parameterized movement primitives have been extensively used for imitati...
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High Acceleration Reinforcement Learning for RealWorld Juggling with Binary Rewards
Robots that can learn in the physical world will be important to enable...
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ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows
We introduce ImitationFlow, a novel Deep generative model that allows le...
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A Differentiable Newton Euler Algorithm for Multibody Model Learning
In this work, we examine a spectrum of hybrid model for the domain of mu...
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Differentiable Implicit Layers
In this paper, we introduce an efficient backpropagation scheme for non...
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Active Inference or Control as Inference? A Unifying View
Active inference (AI) is a persuasive theoretical framework from computa...
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Advances in HumanRobot Handshaking
The use of social, anthropomorphic robots to support humans in various i...
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Assisted Teleoperation in Changing Environments with a Mixture of Virtual Guides
Haptic guidance is a powerful technique to combine the strengths of huma...
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ModelBased QualityDiversity Search for Efficient Robot Learning
Despite recent progress in robot learning, it still remains a challenge ...
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MultiSensor NextBestView Planning as MatroidConstrained Submodular Maximization
3D scene models are useful in robotics for tasks such as path planning, ...
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Convex Regularization in MonteCarlo Tree Search
MonteCarlo planning and Reinforcement Learning (RL) are essential to se...
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Deterministic Inference of Neural Stochastic Differential Equations
Model noise is known to have detrimental effects on neural networks, suc...
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Learning to Play Table Tennis From Scratch using Muscular Robots
Dynamic tasks like table tennis are relatively easy to learn for humans ...
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Reinforcement Learning from a Mixture of Interpretable Experts
Reinforcement learning (RL) has demonstrated its ability to solve high d...
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Orientation Attentive Robot Grasp Synthesis
Physical neighborhoods of grasping points in common objects may offer a ...
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Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation
The control of nonlinear dynamical systems remains a major challenge for...
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SelfPaced Deep Reinforcement Learning
Generalization and reuse of agent behaviour across a variety of learning...
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Deep Reinforcement Learning with Weighted QLearning
Overestimation of the maximum actionvalue is a wellknown problem that ...
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Learning to Fly via Deep ModelBased Reinforcement Learning
Learning to control robots without requiring models has been a longterm...
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Deep Adversarial Reinforcement Learning for Object Disentangling
Deep learning in combination with improved training techniques and high ...
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Bayesian Domain Randomization for SimtoReal Transfer
When learning policies for robot control, the realworld data required i...
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Underactuated Waypoint Trajectory Optimization for Light Painting Photography
Despite their abundance in robotics and nature, underactuated systems re...
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Dimensionality Reduction of Movement Primitives in Parameter Space
Movement primitives are an important policy class for realworld robotic...
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Probabilistic approach to physical object disentangling
Physically disentangling entangled objects from each other is a problem ...
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MetricBased Imitation Learning Between Two Dissimilar Anthropomorphic Robotic Arms
The development of autonomous robotic systems that can learn from human ...
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An Upper Bound of the Bias of NadarayaWatson Kernel Regression under Lipschitz Assumptions
The NadarayaWatson kernel estimator is among the most popular nonparame...
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Evaluation of the Handshake Turing Test for anthropomorphic Robots
Handshakes are fundamental and common greeting and parting gestures amon...
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A Probabilistic Framework for Imitating Human Race Driver Behavior
Understanding and modeling human driver behavior is crucial for advanced...
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A Nonparametric Offpolicy Policy Gradient
Reinforcement learning (RL) algorithms still suffer from high sample com...
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MushroomRL: Simplifying Reinforcement Learning Research
MushroomRL is an opensource Python library developed to simplify the pr...
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LongTerm Visitation Value for Deep Exploration in Sparse Reward Reinforcement Learning
Reinforcement learning with sparse rewards is still an open challenge. C...
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Generalized Mean Estimation in MonteCarlo Tree Search
We consider MonteCarlo Tree Search (MCTS) applied to Markov Decision Pr...
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Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer
A key feature of intelligent behavior is the ability to learn abstract s...
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Receding Horizon Curiosity
Sampleefficient exploration is crucial not only for discovering rewardi...
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Stochastic Optimal Control as Approximate Input Inference
Optimal control of stochastic nonlinear dynamical systems is a major cha...
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SelfPaced Contextual Reinforcement Learning
Generalization and adaptation of learned skills to novel situations is a...
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Building a Library of Tactile Skills Based on FingerVision
Camerabased tactile sensors are emerging as a promising inexpensive sol...
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HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints
Learning optimal feedback control laws capable of executing optimal traj...
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Real Time Trajectory Prediction Using Deep Conditional Generative Models
Data driven methods for time series forecasting that quantify uncertaint...
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Reliable Real Time Ball Tracking for Robot Table Tennis
Robot table tennis systems require a vision system that can track the ba...
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Modelbased Lookahead Reinforcement Learning
Modelbased Reinforcement Learning (MBRL) allows dataefficient learning...
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Experience Reuse with Probabilistic Movement Primitives
Acquiring new robot motor skills is cumbersome, as learning a skill from...
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Assessing Transferability from Simulation to Reality for Reinforcement Learning
Learning robot control policies from physics simulations is of great int...
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Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning
Deep learning has achieved astonishing results on many tasks with large ...
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Deep Lagrangian Networks for endtoend learning of energybased control for underactuated systems
Applying Deep Learning to control has a lot of potential for enabling th...
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Entropic Regularization of Markov Decision Processes
An optimal feedback controller for a given Markov decision process (MDP)...
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Jan Peters
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