4 citations · 5 across the 5 of their papers we have counts for
5 papers
Learning to See While Learning to Act: Diffusion Models for Active Perception in Robot Imitation
Kuancheng Wang, Vaibhav Saxena, Shuo Cheng +2
Most imitation learning methods assume full observability in table-top settings. In practice, objects are often occluded, requiring robots to both search and act, and learning this…
Text to Robotic Assembly of Multi Component Objects using 3D Generative AI and Vision Language Models
Alexander Htet Kyaw, Richa Gupta, Dhruv Shah +8
Advances in 3D generative AI have enabled the creation of physical objects from text prompts, but challenges remain in creating objects involving multiple component types. We prese…
C3DM: Constrained-Context Conditional Diffusion Models for Imitation Learning
Vaibhav Saxena, Yotto Koga, Danfei Xu
Behavior Cloning (BC) methods are effective at learning complex manipulation tasks. However, they are prone to spurious correlation - expressive models may focus on distractors tha…
Generalizable Pose Estimation Using Implicit Scene Representations
Vaibhav Saxena, Kamal Rahimi Malekshan, Linh Tran +1
6-DoF pose estimation is an essential component of robotic manipulation pipelines. However, it usually suffers from a lack of generalization to new instances and object types. Most…
On CAD Informed Adaptive Robotic Assembly
Yotto Koga, Heather Kerrick, Sachin Chitta
We introduce a robotic assembly system that streamlines the design-to-make workflow for going from a CAD model of a product assembly to a fully programmed and adaptive assembly pro…