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Found 22 papers, 10 papers with code
Incremental Learning of Humanoid Robot Behavior from Natural Interaction and Large Language Models
In this paper, we propose a system to achieve incremental learning of complex behavior from natural interaction, and demonstrate its implementation on a humanoid robot.
Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities
This article presents a novel telepresence system for advancing aerial manipulation in dynamic and unstructured environments.
Bringing robotics taxonomies to continuous domains via GPLVM on hyperbolic manifolds
To overcome this problem, we propose to model taxonomy data via hyperbolic embeddings that capture the associated hierarchical structure.
SpeedFolding: Learning Efficient Bimanual Folding of Garments
Folding garments reliably and efficiently is a long standing challenge in robotic manipulation due to the complex dynamics and high dimensional configuration space of garments.
A Memory System of a Robot Cognitive Architecture and its Implementation in ArmarX
We introduce the memory system for our cognitive robot control architecture and its implementation in the robot software framework ArmarX.
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels
Bayesian optimization is a data-efficient technique which can be used for control parameter tuning, parametric policy adaptation, and structure design in robotics.
Graph-based Task-specific Prediction Models for Interactions between Deformable and Rigid Objects
In an ablation study, we show the benefits of the two-stage model for single time step prediction and the effectiveness of the mixed-horizon model for long-term prediction tasks.
Robotics
Learning to Shift Attention for Motion Generation
For multiple modes, we suggest to learn local latent representations of motion trajectories with a density estimation method based on real-valued non-volume preserving (RealNVP) transformations that provides a set of powerful, stably invertible, and learnable transformations.
Object and Relation Centric Representations for Push Effect Prediction
Our approach enables the robot to predict and adapt the effect of a pushing action as it observes the scene.
Uncertainty-aware Contact-safe Model-based Reinforcement Learning
In typical MBRL, we cannot expect the data-driven model to generate accurate and reliable policies to the intended robotic tasks during the learning process due to sample scarcity.
Model-based Reinforcement Learning
reinforcement-learning
+1
Learning Compliance Adaptation in Contact-Rich Manipulation
Compliant robot behavior is crucial for the realization of contact-rich manipulation tasks.
Learning Visual Dynamics Models of Rigid Objects using Relational Inductive Biases
The first, Graph Networks (GN) based approach, considers explicitly defined edge attributes and not only does it consistently underperform an auto-encoder baseline that we modified to predict future states, our results indicate how different edge attributes can significantly influence the predictions.
Noise Regularization for Conditional Density Estimation
To address this issue, we develop a model-agnostic noise regularization method for CDE that adds random perturbations to the data during training.
ProMP: Proximal Meta-Policy Search
Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood.
Model-Based Reinforcement Learning via Meta-Policy Optimization
Finally, we demonstrate that our approach is able to match the asymptotic performance of model-free methods while requiring significantly less experience.
Model-based Reinforcement Learning
reinforcement-learning
+1
Introducing the Simulated Flying Shapes and Simulated Planar Manipulator Datasets
We release two artificial datasets, Simulated Flying Shapes and Simulated Planar Manipulator that allow to test the learning ability of video processing systems.
Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution
We present a novel deep neural network architecture for representing robot experiences in an episodic-like memory which facilitates encoding, recalling, and predicting action experiences.
Parameter Space Noise for Exploration
Combining parameter noise with traditional RL methods allows to combine the best of both worlds.
Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks
We evaluate our approach on 2, 846 human whole-body motions and 6, 187 natural language descriptions thereof from the KIT Motion-Language Dataset.
The KIT Motion-Language Dataset
Linking human motion and natural language is of great interest for the generation of semantic representations of human activities as well as for the generation of robot activities based on natural language input.
Resource-Aware Programming for Robotic Vision
The result indicate that the new programming model together with the extensions within the application layer, makes them highly adaptable; leading to better quality in the results obtained.
Data-Driven Grasp Synthesis - A Survey
In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation.
Robotics
