activity
20182022
most citedHANDS: A Multimodal Dataset for Modeling Towards Human Grasp Intent Inference in Prosthetic Hands

28 citations · 72 across the 8 of their papers we have counts for

collaborators

17 papers

cs.RO202211 cited

Tactile Pose Estimation and Policy Learning for Unknown Object Manipulation

Tarik Kelestemur, Robert Platt, Taskin Padir

Object pose estimation methods allow finding locations of objects in unstructured environments. This is a highly desired skill for autonomous robot manipulation as robots need to e…

cs.RO2021

Reactive Navigation Framework for Mobile Robots by Heuristically Evaluated Pre-sampled Trajectories

Neşet Ünver Akmandor, Taşkın Padır

This paper describes and analyzes a reactive navigation framework for mobile robots in unknown environments. The approach does not rely on a global map and only considers the local…

cs.RO2021

End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learning

Mohammadreza Sharif, Deniz Erdogmus, Christopher Amato +1

State-of-the-art human-in-the-loop robot grasping is hugely suffered by Electromyography (EMG) inference robustness issues. As a workaround, researchers have been looking into inte…

cs.CV20218 cited

From Hand-Perspective Visual Information to Grasp Type Probabilities: Deep Learning via Ranking Labels

Mo Han, Sezen Ya{ğ}mur Günay, İlkay Yıldız +5

Limb deficiency severely affects the daily lives of amputees and drives efforts to provide functional robotic prosthetic hands to compensate this deprivation. Convolutional neural…

cs.RO202128 cited

HANDS: A Multimodal Dataset for Modeling Towards Human Grasp Intent Inference in Prosthetic Hands

Mo Han, Sezen Ya{ğ}mur Günay, Gunar Schirner +2

Upper limb and hand functionality is critical to many activities of daily living and the amputation of one can lead to significant functionality loss for individuals. From this per…

cs.RO20212 cited

Affordance-Based Mobile Robot Navigation Among Movable Obstacles

Maozhen Wang, Rui Luo, Aykut Ozgun Onol +1

Avoiding obstacles in the perceived world has been the classical approach to autonomous mobile robot navigation. However, this usually leads to unnatural and inefficient motions th…