Search Results for author:
Found 27 papers, 12 papers with code
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
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.
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.
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.
Parameter Space Noise for Exploration
Combining parameter noise with traditional RL methods allows to combine the best of both worlds.
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.
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.
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
ProMP: Proximal Meta-Policy Search
Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood.
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.
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.
Learning Compliance Adaptation in Contact-Rich Manipulation
Compliant robot behavior is crucial for the realization of contact-rich manipulation tasks.
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
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.
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.
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
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.
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.
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.
Bringing motion taxonomies to continuous domains via GPLVM on hyperbolic manifolds
This may be attributed to the lack of computational models that fill the gap between the discrete hierarchical structure of the taxonomy and the high-dimensional heterogeneous data associated to its categories.
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.
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.
Unraveling the Single Tangent Space Fallacy: An Analysis and Clarification for Applying Riemannian Geometry in Robot Learning
In the realm of robotics, numerous downstream robotics tasks leverage machine learning methods for processing, modeling, or synthesizing data.
Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges
Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied agents.
How to Raise a Robot -- A Case for Neuro-Symbolic AI in Constrained Task Planning for Humanoid Assistive Robots
Humanoid robots will be able to assist humans in their daily life, in particular due to their versatile action capabilities.
AutoGPT+P: Affordance-based Task Planning with Large Language Models
AutoGPT+P leverages this representation to derive and execute a plan for a task specified by the user in natural language.
Riemannian Flow Matching Policy for Robot Motion Learning
We introduce Riemannian Flow Matching Policies (RFMP), a novel model for learning and synthesizing robot visuomotor policies.
