
An Empirical Analysis of MeasureValued Derivatives for Policy Gradients
Reinforcement learning methods for robotics are increasingly successful ...
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Efficient and Reactive Planning for High Speed Robot Air Hockey
Highly dynamic robotic tasks require highspeed and reactive robots. The...
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HighDimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
We introduce a method based on deep metric learning to perform Bayesian ...
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Robust Value Iteration for Continuous Control Tasks
When transferring a control policy from simulation to a physical system,...
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Evolutionary Training and Abstraction Yields Algorithmic Generalization of Neural Computers
A key feature of intelligent behaviour is the ability to learn abstract ...
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Stochastic Control through Approximate Bayesian Input Inference
Optimal control under uncertainty is a prevailing challenge in control, ...
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Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning
Reactive motion generation problems are usually solved by computing acti...
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Value Iteration in Continuous Actions, States and Time
Classical value iteration approaches are not applicable to environments ...
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Reinforcement Learning using Guided Observability
Due to recent breakthroughs, reinforcement learning (RL) has demonstrate...
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Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via RelativeEntropy Trust Regions
Trajectory optimization and model predictive control are essential techn...
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SKID RAW: Skill Discovery from Raw Trajectories
Integrating robots in complex everyday environments requires a multitude...
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Model Predictive ActorCritic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning
Substantial advancements to modelbased reinforcement learning algorithm...
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Advancing Trajectory Optimization with Approximate Inference: Exploration, Covariance Control and Adaptive Risk
Discretetime stochastic optimal control remains a challenging problem f...
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Extended Task and Motion Planning of Longhorizon Robot Manipulation
Task and Motion Planning (TAMP) requires the integration of symbolic rea...
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Learning Humanlike Hand Reaching for HumanRobot Handshaking
One of the first and foremost nonverbal interactions that humans perfor...
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A Probabilistic Interpretation of SelfPaced Learning with Applications to Reinforcement Learning
Across machine learning, the use of curricula has shown strong empirical...
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HumanRobot Handshaking: A Review
For some years now, the use of social, anthropomorphic robots in various...
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Structured Policy Representation: Imposing Stability in arbitrarily conditioned dynamic systems
We present a new family of deep neural networkbased dynamic systems. Th...
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Perspectives on Sim2Real Transfer for Robotics: A Summary of the R:SS 2020 Workshop
This report presents the debates, posters, and discussions of the Sim2Re...
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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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Jan Peters
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