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Found 18 papers, 12 papers with code
MuTT: A Multimodal Trajectory Transformer for Robot Skills
High-level robot skills represent an increasingly popular paradigm in robot programming.
KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty
We propose KalMamba, an efficient architecture to learn representations for RL that combines the strengths of probabilistic SSMs with the scalability of deterministic SSMs.
Iterative Sizing Field Prediction for Adaptive Mesh Generation From Expert Demonstrations
To balance computational speed and accuracy meshes with adaptive resolution are used, allocating more resources to critical parts of the geometry.
Vlearn: Off-Policy Learning with Efficient State-Value Function Estimation
This transfer to deep methods is not straightforward and requires novel design choices such as robust policy updates, twin value function networks to avoid an optimization bias, and importance weight clipping.
Information-Theoretic Trust Regions for Stochastic Gradient-Based Optimization
Before each update, it solves the trust region problem for an optimal step size, resulting in a more stable and faster optimization process.
Curriculum-Based Imitation of Versatile Skills
Learning skills by imitation is a promising concept for the intuitive teaching of robots.
Combining Reconstruction and Contrastive Methods for Multimodal Representations in RL
Here, different self-supervised loss functions have distinct advantages and limitations depending on the information density of the underlying sensor modality.
On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning
We show that RSSMs use a suboptimal inference scheme and that models trained using this inference overestimate the aleatoric uncertainty of the ground truth system.
Model-based Reinforcement Learning
reinforcement-learning
+3
Inferring Versatile Behavior from Demonstrations by Matching Geometric Descriptors
We find that the geometric descriptors greatly help in generalizing to new task configurations and that combining them with our distribution-matching objective is crucial for representing and reproducing versatile behavior.
Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios
Recurrent State-space models (RSSMs) are highly expressive models for learning patterns in time series data and system identification.
End-to-End Learning of Hybrid Inverse Dynamics Models for Precise and Compliant Impedance Control
These models need to precisely capture the robot dynamics, which consist of well-understood components, e. g., rigid body dynamics, and effects that remain challenging to capture, e. g., stick-slip friction and mechanical flexibilities.
Specializing Versatile Skill Libraries using Local Mixture of Experts
This local and incremental learning results in a modular MoE model of high accuracy and versatility, where both properties can be scaled by adding more components on the fly.
Switching Recurrent Kalman Networks
In addition, driving data can often be multimodal in distribution, meaning that there are distinct predictions that are likely, but averaging can hurt model performance.
Versatile Inverse Reinforcement Learning via Cumulative Rewards
Inverse Reinforcement Learning infers a reward function from expert demonstrations, aiming to encode the behavior and intentions of the expert.
Differentiable Trust Region Layers for Deep Reinforcement Learning
However, enforcing such trust regions in deep reinforcement learning is difficult.
Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning
We adopt a recent probabilistic recurrent neural network architecture, called Re-current Kalman Networks (RKNs), to model learning by conditioning its transition dynamics on the control actions.
Expected Information Maximization: Using the I-Projection for Mixture Density Estimation
Such behavior is appealing whenever we deal with highly multi-modal data where modelling single modes correctly is more important than covering all the modes.
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
In order to integrate uncertainty estimates into deep time-series modelling, Kalman Filters (KFs) (Kalman et al., 1960) have been integrated with deep learning models, however, such approaches typically rely on approximate inference techniques such as variational inference which makes learning more complex and often less scalable due to approximation errors.
