46 citations · 88 across the 15 of their papers we have counts for
16 papers
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…
ARMBench: An Object-centric Benchmark Dataset for Robotic Manipulation
Chaitanya Mitash, Fan Wang, Shiyang Lu +4
This paper introduces Amazon Robotic Manipulation Benchmark (ARMBench), a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse. Auto…
Online Object Model Reconstruction and Reuse for Lifelong Improvement of Robot Manipulation
Shiyang Lu, Rui Wang, Yinglong Miao +2
This work proposes a robotic pipeline for picking and constrained placement of objects without geometric shape priors. Compared to recent efforts developed for similar tasks, where…
Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
Bowen Wen, Chaitanya Mitash, Kostas Bekris
Tracking the 6D pose of objects in video sequences is important for robot manipulation. This work presents se(3)-TrackNet, a data-driven optimization approach for long term, 6D pos…
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…
se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
Bowen Wen, Chaitanya Mitash, Baozhang Ren +1
Tracking the 6D pose of objects in video sequences is important for robot manipulation. This task, however, introduces multiple challenges: (i) robot manipulation involves signific…
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…
Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive Hands
Bowen Wen, Chaitanya Mitash, Sruthi Soorian +3
Many manipulation tasks, such as placement or within-hand manipulation, require the object's pose relative to a robot hand. The task is difficult when the hand significantly occlud…
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…
Tight Robot Packing in the Real World: A Complete Manipulation Pipeline with Robust Primitives
Rahul Shome, Wei N. Tang, Changkyu Song +5
Many order fulfillment applications in logistics, such as packing, involve picking objects from unstructured piles before tightly arranging them in bins or shipping containers. Des…
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…
Learning Object Localization and 6D Pose Estimation from Simulation and Weakly Labeled Real Images
Jean-Philippe Mercier, Chaitanya Mitash, Philippe Giguère +1
This work proposes a process for efficiently training a point-wise object detector that enables localizing objects and computing their 6D poses in cluttered and occluded scenes. Ac…
Robust 6D Object Pose Estimation with Stochastic Congruent Sets
Chaitanya Mitash, Abdeslam Boularias, Kostas Bekris
Object pose estimation is frequently achieved by first segmenting an RGB image and then, given depth data, registering the corresponding point cloud segment against the object's 3D…
Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search
Chaitanya Mitash, Abdeslam Boularias, Kostas E. Bekris
This work proposes a process for efficiently searching over combinations of individual object 6D pose hypotheses in cluttered scenes, especially in cases involving occlusions and o…
A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation
Chaitanya Mitash, Kostas E. Bekris, Abdeslam Boularias
Progress has been achieved recently in object detection given advancements in deep learning. Nevertheless, such tools typically require a large amount of training data and signific…