activity
20192026
most citedApproximating Constraint Manifolds Using Generative Models for Sampling-Based Constrained Motion Planning

10 citations · 23 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2026

Efficient Text-to-Image Generation: An Adaptive Step Schedule Controller for Diffusion Models

Kuluhan Binici, Cihan Acar, Shivam Aggarwal +2

Text-to-image diffusion models often use a fixed number of denoising steps, balancing time costs and image quality. However, the optimal number of steps depends on the complexity o…

cs.RO2023★ 4 cited

Visual-Policy Learning through Multi-Camera View to Single-Camera View Knowledge Distillation for Robot Manipulation Tasks

Cihan Acar, Kuluhan Binici, Alp Tekirdağ +1

The use of multi-camera views simultaneously has been shown to improve the generalization capabilities and performance of visual policies. However, the hardware cost and design con…

cs.RO2022

Visuo-Tactile Manipulation Planning Using Reinforcement Learning with Affordance Representation

Wenyu Liang, Fen Fang, Cihan Acar +4

Robots are increasingly expected to manipulate objects in ever more unstructured environments where the object properties have high perceptual uncertainty from any single sensory m…

cs.RO2022★ 1 cited

GloCAL: Glocalized Curriculum-Aided Learning of Multiple Tasks with Application to Robotic Grasping

Anil Kurkcu, Cihan Acar, Domenico Campolo +1

The domain of robotics is challenging to apply deep reinforcement learning due to the need for large amounts of data and for ensuring safety during learning. Curriculum learning ha…

cs.RO2022★ 10 cited

Approximating Constraint Manifolds Using Generative Models for Sampling-Based Constrained Motion Planning

Cihan Acar, Keng Peng Tee

Sampling-based motion planning under task constraints is challenging because the null-measure constraint manifold in the configuration space makes rejection sampling extremely inef…

cs.CV2019★ 8 cited

6D Pose Estimation with Correlation Fusion

Yi Cheng, Hongyuan Zhu, Ying Sun +6

6D object pose estimation is widely applied in robotic tasks such as grasping and manipulation. Prior methods using RGB-only images are vulnerable to heavy occlusion and poor illum…