activity
20222026
most citedWhat you see is (not) what you get: A VR Framework for Correcting Robot Errors

6 citations · 17 across the 10 of their papers we have counts for

collaborators

11 papers

cs.CV2026

One Diffusion Model, Two Roles: Guided Trajectory Planning and Safety-Critical Scenario Generation in Closed-Loop Simulation

Arka Pal, Rajesh Kumar, Hannes Eriksson +4

Diffusion probabilistic models can capture the multi-modal, interaction-rich distribution of joint future trajectories in driving scenes. We show that a single pretrained diffusion…

cs.CV2025

BlendCLIP: Bridging Synthetic and Real Domains for Zero-Shot 3D Object Classification with Multimodal Pretraining

Ajinkya Khoche, Gergő László Nagy, Maciej Wozniak +2

Zero-shot 3D object classification is crucial for real-world applications like autonomous driving, however it is often hindered by a significant domain gap between the synthetic da…

cs.CV2025

PRIX: Learning to Plan from Raw Pixels for End-to-End Autonomous Driving

Maciej K. Wozniak, Lianhang Liu, Yixi Cai +1

While end-to-end autonomous driving models show promising results, their practical deployment is often hindered by large model sizes, a reliance on expensive LiDAR sensors and comp…

cs.RO2024

Low-Cost Teleoperation with Haptic Feedback through Vision-based Tactile Sensors for Rigid and Soft Object Manipulation

Martina Lippi, Michael C. Welle, Maciej K. Wozniak +2

Haptic feedback is essential for humans to successfully perform complex and delicate manipulation tasks. A recent rise in tactile sensors has enabled robots to leverage the sense o…

cs.RO2024★ 4 cited

MCD: Diverse Large-Scale Multi-Campus Dataset for Robot Perception

Thien-Minh Nguyen, Shenghai Yuan, Thien Hoang Nguyen +8

Perception plays a crucial role in various robot applications. However, existing well-annotated datasets are biased towards autonomous driving scenarios, while unlabelled SLAM data…

cs.CV2024★ 1 cited

UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gaps

Maciej K Wozniak, Mattias Hansson, Marko Thiel +1

In this study, we address a gap in existing unsupervised domain adaptation approaches on LiDAR-based 3D object detection, which have predominantly concentrated on adapting between…