15 citations · 15 across the 5 of their papers we have counts for
7 papers · 1 filter
Demonstrating Multi-Suction Item Picking at Scale via Multi-Modal Learning of Pick Success
Che Wang, Jeroen van Baar, Chaitanya Mitash +6
This work demonstrates how autonomously learning aspects of robotic operation from sparsely-labeled, real-world data of deployed, engineered solutions at industrial scale can provi…
Safe and Effective Picking Paths in Clutter given Discrete Distributions of Object Poses
Rui Wang, Chaitanya Mitash, Shiyang Lu +2
Picking an item in the presence of other objects can be challenging as it involves occlusions and partial views. Given object models, one approach is to perform object pose estimat…
Task-driven Perception and Manipulation for Constrained Placement of Unknown Objects
Chaitanya Mitash, Rahul Shome, Bowen Wen +2
Recent progress in robotic manipulation has dealt with the case of previously unknown objects in the context of relatively simple tasks, such as bin-picking. Existing methods for m…
That and There: Judging the Intent of Pointing Actions with Robotic Arms
Malihe Alikhani, Baber Khalid, Rahul Shome +3
Collaborative robotics requires effective communication between a robot and a human partner. This work proposes a set of interpretive principles for how a robotic arm can use point…
Scene-level Pose Estimation for Multiple Instances of Densely Packed Objects
Chaitanya Mitash, Bowen Wen, Kostas Bekris +1
This paper introduces key machine learning operations that allow the realization of robust, joint 6D pose estimation of multiple instances of objects either densely packed or in un…
Physics-based Scene-level Reasoning for Object Pose Estimation in Clutter
Chaitanya Mitash, Abdeslam Boularias, Kostas Bekris
This paper focuses on vision-based pose estimation for multiple rigid objects placed in clutter, especially in cases involving occlusions and objects resting on each other. Progres…