102 citations · 113 across the 15 of their papers we have counts for
10 papers · 1 filter
TACTIC: Understanding Tactile Encoders and Conditioning for Contact-rich Robot Manipulation Policies
Seongjin Bien, Débora Oliveira Makowski, Carlo Kneissl +6
Tactile information is essential for contact-rich manipulation tasks in robotics. Vision-based tactile sensors make it particularly easy to design end-to-end manipulation policies…
Norm2Tex: Augmenting Visuo-Tactile Simulations with Texture
Seongjin Bien, Débora Oliveira Makowski, Roberto Calandra +2
Large-scale datasets are essential for training generalist robot control policies. Collecting real-world tactile data is costly and time-consuming, motivating the use of tactile si…
DuoBench: A Reproducible Benchmark for Bimanual Manipulation in Simulation and the Real World
Tobias Jülg, Seongjin Bien, Simon Hilber +7
Bimanual robot systems substantially expand manipulation capabilities, but coordinating two arms introduces additional control complexity and failure modes that are not well captur…
Rewarding DINO: Predicting Dense Rewards with Vision Foundation Models
Pierre Krack, Tobias Jülg, Wolfram Burgard +1
Well-designed dense reward functions in robot manipulation not only indicate whether a task is completed but also encode progress along the way. Generally, designing dense rewards…
VLAgents: A Policy Server for Efficient VLA Inference
Tobias Jülg, Khaled Gamal, Nisarga Nilavadi +5
The rapid emergence of Vision-Language-Action models (VLAs) has a significant impact on robotics. However, their deployment remains complex due to the fragmented interfaces and the…
Robot Control Stack: A Lean Ecosystem for Robot Learning at Scale
Tobias Jülg, Pierre Krack, Seongjin Bien +7
Vision-Language-Action models (VLAs) mark a major shift in robot learning. They replace specialized architectures and task-tailored components of expert policies with large-scale d…
Augmented Reality for RObots (ARRO): Pointing Visuomotor Policies Towards Visual Robustness
Reihaneh Mirjalili, Tobias Jülg, Florian Walter +1
Visuomotor policies trained on human expert demonstrations have recently shown strong performance across a wide range of robotic manipulation tasks. However, these policies remain…
iPack: Intuitive Bin Packing with Large Language Models
Yannik Blei, Michael Krawez, Adrian Göß +5
Robotics and automation are increasingly influential in logistics but remain largely confined to traditional warehouses. In grocery retail, advancements such as cashier-less superm…
Refined Policy Distillation: From VLA Generalists to RL Experts
Tobias Jülg, Wolfram Burgard, Florian Walter
Vision-Language-Action Models (VLAs) have demonstrated remarkable generalization capabilities in real-world experiments. However, their success rates are often not on par with expe…
VLM-Vac: Enhancing Smart Vacuums through VLM Knowledge Distillation and Language-Guided Experience Replay
Reihaneh Mirjalili, Michael Krawez, Florian Walter +1
In this paper, we propose VLM-Vac, a novel framework designed to enhance the autonomy of smart robot vacuum cleaners. Our approach integrates the zero-shot object detection capabil…