34 citations · 47 across the 8 of their papers we have counts for
9 papers
Mash, Spread, Slice! Learning to Manipulate Object States via Visual Spatial Progress
Priyanka Mandikal, Jiaheng Hu, Shivin Dass +3
Most robot manipulation focuses on changing the kinematic state of objects: picking, placing, opening, or rotating them. However, a wide range of real-world manipulation tasks invo…
SPOC: Spatially-Progressing Object State Change Segmentation in Video
Priyanka Mandikal, Tushar Nagarajan, Alex Stoken +2
Object state changes in video reveal critical cues about human and agent activity. However, existing methods are limited to temporal localization of when the object is in its initi…
ScrewMimic: Bimanual Imitation from Human Videos with Screw Space Projection
Arpit Bahety, Priyanka Mandikal, Ben Abbatematteo +1
Bimanual manipulation is a longstanding challenge in robotics due to the large number of degrees of freedom and the strict spatial and temporal synchronization required to generate…
DexVIP: Learning Dexterous Grasping with Human Hand Pose Priors from Video
Priyanka Mandikal, Kristen Grauman
Dexterous multi-fingered robotic hands have a formidable action space, yet their morphological similarity to the human hand holds immense potential to accelerate robot learning. We…
Learning Dexterous Grasping with Object-Centric Visual Affordances
Priyanka Mandikal, Kristen Grauman
Dexterous robotic hands are appealing for their agility and human-like morphology, yet their high degree of freedom makes learning to manipulate challenging. We introduce an approa…
Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network
Priyanka Mandikal, R. Venkatesh Babu
Reconstructing a high-resolution 3D model of an object is a challenging task in computer vision. Designing scalable and light-weight architectures is crucial while addressing this…
CAPNet: Continuous Approximation Projection For 3D Point Cloud Reconstruction Using 2D Supervision
Navaneet K L, Priyanka Mandikal, Mayank Agarwal +1
Knowledge of 3D properties of objects is a necessity in order to build effective computer vision systems. However, lack of large scale 3D datasets can be a major constraint for dat…
3D-PSRNet: Part Segmented 3D Point Cloud Reconstruction From a Single Image
Priyanka Mandikal, Navaneet K L, R. Venkatesh Babu
We propose a mechanism to reconstruct part annotated 3D point clouds of objects given just a single input image. We demonstrate that jointly training for both reconstruction and se…
3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Image
Priyanka Mandikal, K L Navaneet, Mayank Agarwal +1
3D reconstruction from single view images is an ill-posed problem. Inferring the hidden regions from self-occluded images is both challenging and ambiguous. We propose a two-pronge…