3 citations · 7 across the 15 of their papers we have counts for
15 papers
Lifelong Multi-Subsystem Pickup and Delivery with Buffer-Limited Handover Stations
Chuanlong Zang, Isabelle Barz, Anna Mannucci +3
Coordinating payload transfers between subsystems is a critical challenge in lifelong Multi-Agent Pickup and Delivery (MAPD). We study systems where agents are confined to separate…
GRACE: A Unified 2D Multi-Robot Path Planning Simulator & Benchmark for Grid, Roadmap, And Continuous Environments
Chuanlong Zang, Anna Mannucci, Isabelle Barz +3
Advancing Multi-Agent Pathfinding (MAPF) and Multi-Robot Motion Planning (MRMP) requires platforms that enable transparent, reproducible comparisons across modeling choices. Existi…
Adaptive Domain Modeling with Language Models: A Multi-Agent Approach to Task Planning
Harisankar Babu, Philipp Schillinger, Tamim Asfour
We introduce TAPAS (Task-based Adaptation and Planning using AgentS), a multi-agent framework that integrates Large Language Models (LLMs) with symbolic planning to solve complex t…
Diffeomorphic Obstacle Avoidance for Contractive Dynamical Systems via Implicit Representations
Ken-Joel Simmoteit, Philipp Schillinger, Leonel Rozo
Ensuring safety and robustness of robot skills is becoming crucial as robots are required to perform increasingly complex and dynamic tasks. The former is essential when performing…
Efficient End-to-End Detection of 6-DoF Grasps for Robotic Bin Picking
Yushi Liu, Alexander Qualmann, Zehao Yu +5
Bin picking is an important building block for many robotic systems, in logistics, production or in household use-cases. In recent years, machine learning methods for the predictio…
Pseudo-Labeling and Contextual Curriculum Learning for Online Grasp Learning in Robotic Bin Picking
Huy Le, Philipp Schillinger, Miroslav Gabriel +2
The prevailing grasp prediction methods predominantly rely on offline learning, overlooking the dynamic grasp learning that occurs during real-time adaptation to novel picking scen…
Uncertainty-driven Exploration Strategies for Online Grasp Learning
Yitian Shi, Philipp Schillinger, Miroslav Gabriel +4
Existing grasp prediction approaches are mostly based on offline learning, while, ignoring the exploratory grasp learning during online adaptation to new picking scenarios, i.e., o…
Model-free Grasping with Multi-Suction Cup Grippers for Robotic Bin Picking
Philipp Schillinger, Miroslav Gabriel, Alexander Kuss +2
This paper presents a novel method for model-free prediction of grasp poses for suction grippers with multiple suction cups. Our approach is agnostic to the design of the gripper a…
The e-Bike Motor Assembly: Towards Advanced Robotic Manipulation for Flexible Manufacturing
Leonel Rozo, Andras G. Kupcsik, Philipp Schillinger +9
Robotic manipulation is currently undergoing a profound paradigm shift due to the increasing needs for flexible manufacturing systems, and at the same time, because of the advances…
Learning Dense Visual Descriptors using Image Augmentations for Robot Manipulation Tasks
Christian Graf, David B. Adrian, Joshua Weil +5
We propose a self-supervised training approach for learning view-invariant dense visual descriptors using image augmentations. Unlike existing works, which often require complex da…
Optimizing Demonstrated Robot Manipulation Skills for Temporal Logic Constraints
Akshay Dhonthi, Philipp Schillinger, Leonel Rozo +1
For performing robotic manipulation tasks, the core problem is determining suitable trajectories that fulfill the task requirements. Various approaches to compute such trajectories…
Flexible Behavior Trees: In search of the mythical HFSMBTH for Collaborative Autonomy in Robotics
Joshua M. Zutell, David C. Conner, Philipp Schillinger
In recent years, the model of computation known as Behavior Trees (BT), first developed in the video game industry, has become more popular in the robotics community for defining d…
Study of Signal Temporal Logic Robustness Metrics for Robotic Tasks Optimization
Akshay Dhonthi, Philipp Schillinger, Leonel Rozo +1
Signal Temporal Logic (STL) is an efficient technique for describing temporal constraints. It can play a significant role in robotic manipulation, for example, to optimize the robo…
Supervised Training of Dense Object Nets using Optimal Descriptors for Industrial Robotic Applications
Andras Kupcsik, Markus Spies, Alexander Klein +4
Dense Object Nets (DONs) by Florence, Manuelli and Tedrake (2018) introduced dense object descriptors as a novel visual object representation for the robotics community. It is suit…
Learning and Sequencing of Object-Centric Manipulation Skills for Industrial Tasks
Leonel Rozo, Meng Guo, Andras G. Kupcsik +8
Enabling robots to quickly learn manipulation skills is an important, yet challenging problem. Such manipulation skills should be flexible, e.g., be able adapt to the current works…