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From the 1 of 14 linked papers with an AI index.

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14 papers

cs.CV2026

Still image and spatial-temporal tomato data enabling detection, segmentation, tracking, and video-instance segmentation using strong and weak labels

Michael Halstead, Esra Guclu, Mohamed Farag +7

The paper introduces two new datasets of tomato plants captured by a robot—still images (BUTom21) and video sequences (BUTom-ST21)—with pixel‑level annotations for fruit detection,…

cs.RO2026

Implicit-Behavior Coordination from Unlabeled Sub-Task Demonstrations for Rearrangement Tasks

Ahmed Shokry, Usama Ahmed Siddiquie, Sicong Pan +1

Long-horizon robotic rearrangement tasks are often treated as skill sequencing problems, requiring predefined skills, skill labels, or boundaries, and task-specific switching logic…

cs.CV2026

Privacy-Preserving Depth-Only Open-Vocabulary 3D Semantic Segmentation Via Uncertainty-Guided Test-Time Optimization

Xuying Huang, Sicong Pan, Maren Bennewitz

Privacy-preserving perception is a critical requirement for deploying 3D scene understanding systems in real-world indoor environments, yet it remains underexplored in open-vocabul…

cs.RO2026

Efficient Trajectory Optimization for Autonomous Racing via Formula-1 Data-Driven Initialization

Samir Shehadeh, Lukas Kutsch, Nils Dengler +2

Trajectory optimization is a central component of fast and efficient autonomous racing. However practical optimization pipelines remain highly sensitive to initialization and may c…

cs.RO2026

ObjView-Bench: Rethinking Difficulty and Deployment for Object-Centric View Planning

Sicong Pan, Hao Hu, Xuying Huang +2

Object-centric view planning is a core component of active geometric 3D reconstruction in robotics, yet existing evaluations often conflate object complexity, planning difficulty,…

cs.RO2026

Constraint-Aware Reinforcement Learning via Adaptive Action Scaling

Murad Dawood, Usama Ahmed Siddiquie, Shahram Khorshidi +1

Safe reinforcement learning (RL) seeks to mitigate unsafe behaviors that arise from exploration during training by reducing constraint violations while maintaining task performance…