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Found 19 papers, 4 papers with code
BANSAI: Towards Bridging the AI Adoption Gap in Industrial Robotics with Neurosymbolic Programming
Over the past decade, deep learning helped solve manipulation problems across all domains of robotics.
RoboGrind: Intuitive and Interactive Surface Treatment with Industrial Robots
Surface treatment tasks such as grinding, sanding or polishing are a vital step of the value chain in many industries, but are notoriously challenging to automate.
Translating Universal Scene Descriptions into Knowledge Graphs for Robotic Environment
Robots performing human-scale manipulation tasks require an extensive amount of knowledge about their surroundings in order to perform their actions competently and human-like.
Integrating Transformations in Probabilistic Circuits
This study addresses the predictive limitation of probabilistic circuits and introduces transformations as a remedy to overcome it.
Towards a Neuronally Consistent Ontology for Robotic Agents
It therefore employs a shared ontology to model the activity of both kinds of agents, empowering robots to learn from human experiences.
Knowledge-Driven Robot Program Synthesis from Human VR Demonstrations
Aging societies, labor shortages and increasing wage costs call for assistance robots capable of autonomously performing a wide array of real-world tasks.
From Interactive to Co-Constructive Task Learning
Humans have developed the capability to teach relevant aspects of new or adapted tasks to a social peer with very few task demonstrations by making use of scaffolding strategies that leverage prior knowledge and importantly prior joint experience to yield a joint understanding and a joint execution of the required steps to solve the task.
Joint Probability Trees
We introduce Joint Probability Trees (JPT), a novel approach that makes learning of and reasoning about joint probability distributions tractable for practical applications.
Heuristic-free Optimization of Force-Controlled Robot Search Strategies in Stochastic Environments
In both industrial and service domains, a central benefit of the use of robots is their ability to quickly and reliably execute repetitive tasks.
Empirical Estimates on Hand Manipulation are Recoverable: A Step Towards Individualized and Explainable Robotic Support in Everyday Activities
Specifically, we propose and explore the feasibility of structural causal models with non-parametric estimators to derive empirical estimates on hand behavior in the context of object manipulation in a virtual kitchen scenario.
Robot Program Parameter Inference via Differentiable Shadow Program Inversion
To this end, we present Shadow Program Inversion (SPI), a novel approach to infer optimal skill parameters directly from data.
URoboSim -- An Episodic Simulation Framework for Prospective Reasoning in Robotic Agents
We show the capabilities of URoboSim in form of mental simulations, generating data for machine learning and the usage as belief state for a real robot.
Automated acquisition of structured, semantic models of manipulation activities from human VR demonstration
In this paper we present a system capable of collecting and annotating, human performed, robot understandable, everyday activities from virtual environments.
RoboSherlock: Cognition-enabled Robot Perception for Everyday Manipulation Tasks
The application of the UIM principle supports the implementation of perception systems that can answer task-relevant queries about objects in a scene, boost object recognition performance by combining the strengths of multiple perception algorithms, support knowledge-enabled reasoning about objects and enable automatic and knowledge-driven generation of processing pipelines.
Robotics
Amortized Object and Scene Perception for Long-term Robot Manipulation
Mobile robots, performing long-term manipulation activities in human environments, have to perceive a wide variety of objects possessing very different visual characteristics and need to reliably keep track of these throughout the execution of a task.
KnowRob 2.0 — A 2nd Generation Knowledge Processing Framework for Cognition-enabled Robotic Agents
Abstract— In this paper we present K NOW R OB 2, a second generation knowledge representation and reasoning framework for robotic agents.
Reasoning about Unmodelled Concepts - Incorporating Class Taxonomies in Probabilistic Relational Models
We show that by exploiting this structure, probability distributions can be represented more compactly and that the reasoning systems become capable of reasoning about concepts not contained in the probabilistic knowledge base.
Learning and Reasoning with Action-Related Places for Robust Mobile Manipulation
We propose the concept of Action-Related Place (ARPlace) as a powerful and flexible representation of task-related place in the context of mobile manipulation.
Fast Point Feature Histograms (FPFH) for 3D Registration
In our recent work [1], [2], we proposed Point Feature Histograms (PFH) as robust multi-dimensional features which describe the local geometry around a point p for 3D point cloud datasets.
