36 citations · 39 across the 5 of their papers we have counts for
5 papers
TensoIR: Tensorial Inverse Rendering
Haian Jin, Isabella Liu, Peijia Xu +6
We propose TensoIR, a novel inverse rendering approach based on tensor factorization and neural fields. Unlike previous works that use purely MLP-based neural fields, thus sufferin…
Learning Robot Manipulation from Cross-Morphology Demonstration
Gautam Salhotra, I-Chun Arthur Liu, Gaurav Sukhatme
Some Learning from Demonstrations (LfD) methods handle small mismatches in the action spaces of the teacher and student. Here we address the case where the teacher's morphology is…
Learning Deformable Object Manipulation from Expert Demonstrations
Gautam Salhotra, I-Chun Arthur Liu, Marcus Dominguez-Kuhne +1
We present a novel Learning from Demonstration (LfD) method, Deformable Manipulation from Demonstrations (DMfD), to solve deformable manipulation tasks using states or images as in…
ActiveZero: Mixed Domain Learning for Active Stereovision with Zero Annotation
Isabella Liu, Edward Yang, Jianyu Tao +5
Traditional depth sensors generate accurate real world depth estimates that surpass even the most advanced learning approaches trained only on simulation domains. Since ground trut…
Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation
I-Chun Arthur Liu, Shagun Uppal, Gaurav S. Sukhatme +3
Learning complex manipulation tasks in realistic, obstructed environments is a challenging problem due to hard exploration in the presence of obstacles and high-dimensional visual…