16 citations · 16 across the 5 of their papers we have counts for
5 papers
Geometrically Plausible Object Pose Refinement using Differentiable Simulation
Anil Zeybek, Rhys Newbury, Snehal Dikhale +3
State-of-the-art object pose estimation methods are prone to generating geometrically infeasible pose hypotheses. This problem is prevalent in dexterous manipulation, where estimat…
VisuoTactile 6D Pose Estimation of an In-Hand Object using Vision and Tactile Sensor Data
Snehal s. Dikhale, Karankumar Patel, Daksh Dhingra +4
Knowledge of the 6D pose of an object can benefit in-hand object manipulation. In-hand 6D object pose estimation is challenging because of heavy occlusion produced by the robot's g…
HyperTaxel: Hyper-Resolution for Taxel-Based Tactile Signals Through Contrastive Learning
Hongyu Li, Snehal Dikhale, Jinda Cui +2
To achieve dexterity comparable to that of humans, robots must intelligently process tactile sensor data. Taxel-based tactile signals often have low spatial-resolution, with non-st…
ViHOPE: Visuotactile In-Hand Object 6D Pose Estimation with Shape Completion
Hongyu Li, Snehal Dikhale, Soshi Iba +1
In this letter, we introduce ViHOPE, a novel framework for estimating the 6D pose of an in-hand object using visuotactile perception. Our key insight is that the accuracy of the 6D…
Hierarchical Graph Neural Networks for Proprioceptive 6D Pose Estimation of In-hand Objects
Alireza Rezazadeh, Snehal Dikhale, Soshi Iba +1
Robotic manipulation, in particular in-hand object manipulation, often requires an accurate estimate of the object's 6D pose. To improve the accuracy of the estimated pose, state-o…