
Learning Navigation Skills for Legged Robots with Learned Robot Embeddings
Navigation policies are commonly learned on idealized cylinder agents in...
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Learning Extended Body Schemas from Visual Keypoints for Object Manipulation
Humans have impressive generalization capabilities when it comes to mani...
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Leveraging Forward Model Prediction Error for Learning Control
Learning for model based control can be sampleefficient and generalize ...
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Exploring ZeroShot Emergent Communication in Embodied MultiAgent Populations
Effective communication is an important skill for enabling information e...
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ModelBased Inverse Reinforcement Learning from Visual Demonstrations
Scaling modelbased inverse reinforcement learning (IRL) to real robotic...
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Planning in Learned Latent Action Spaces for Generalizable Legged Locomotion
Hierarchical learning has been successful at learning generalizable loco...
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Residual Learning from Demonstration
Contacts and friction are inherent to nearly all robotic manipulation ta...
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Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed
Robots need to be able to adapt to unexpected changes in the environment...
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Adversarial Continual Learning
Continual learning aims to learn new tasks without forgetting previously...
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Learning StateDependent Losses for Inverse Dynamics Learning
Being able to quickly adapt to changes in dynamics is paramount in model...
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Encoding Physical Constraints in Differentiable NewtonEuler Algorithm
The recursive NewtonEuler Algorithm (RNEA) is a popular technique in ro...
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Generalized Inner Loop MetaLearning
Many (but not all) approaches selfqualifying as "metalearning" in deep...
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Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning
Learning to locomote to arbitrary goals on hardware remains a challengin...
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MetaLearning via Learned Loss
We present a metalearning approach based on learning an adaptive, high...
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Curious iLQR: Resolving Uncertainty in Modelbased RL
Curiosity as a means to explore during reinforcement learning problems h...
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A Hierarchical Bayesian Linear Regression Model with Local Features for Stochastic Dynamics Approximation
One of the challenges in modelbased control of stochastic dynamical sys...
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Using Simulation to Improve SampleEfficiency of Bayesian Optimization for Bipedal Robots
Learning for control can acquire controllers for novel robotic tasks, pa...
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Learning Sensor Feedback Models from Demonstrations via PhaseModulated Neural Networks
In order to robustly execute a task under environmental uncertainty, a r...
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A New Data Source for Inverse Dynamics Learning
Modern robotics is gravitating toward increasingly collaborative human r...
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SE3PoseNets: Structured Deep Dynamics Models for Visuomotor Planning and Control
In this work, we present an approach to deep visuomotor control using st...
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Robust Gaussian Filtering using a Pseudo Measurement
Many sensors, such as range, sonar, radar, GPS and visual devices, produ...
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Franziska Meier
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