102 citations · 113 across the 15 of their papers we have counts for
5 papers · 2 filters
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…