Chaitanya Mitash

I am a Ph.D Candidate in the Computer Science department at Rutgers, co-advised by professors Abdeslam Boularias and Kostas Bekris . My research interests lie in the field of Robotics, Computer Vision and Machine Learning. Specifically, I am working on problems related to perception for Robotic Manipulation.
During my PhD, I did internships with Microsoft Hololens, Redmond, WA where I worked on semantic segmentation for mixed reality applications and at Amazon Robotics, North Reading, MA where I worked on task-driven perception for manipulation.
Before graduate school, I worked as a software engineer in the R&D divisions of Samsung Electronics and Tejas Networks in the embedded software domain. I received my B.E. degree in Computer Science from Birla Institute of Technology, Mesra in 2012.

Email  /  CV  /  Google Scholar  /  GitHub  /  LinkedIn

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Research
Task-driven Perception and Manipulation for Constrained Placement of Unknown Objects
Chaitanya Mitash, Rahul Shome, Bowen Wen, Abdeslam Boularias, Kostas Bekris
IEEE Robotics and Automation Letters (RA-L) & IROS, 2020
project page/ arXiv/ supplementary material

An integrated perception and manipulation planning pipeline with a dynamic object representation, demonstrating high success rate and efficiency in solving pick-and-constrained placement of previously unseen objects.

se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
Bowen Wen, Chaitanya Mitash, Baozhang Ren and Kostas Bekris
IEEE International Conference on Intelligent Robots and Systems (IROS), 2020
project page (Coming soon...)/ arXiv (Coming soon...)

Learning in simulation to predict residual poses for object models in RGB-D sequences.

Safe and Effective Picking Paths in Clutter given Discrete Distributions of Object Poses
Rui Wang, Chaitanya Mitash, Shiyang Lu, Daniel Boehm, Kostas E. Bekris
IEEE International Conference on Intelligent Robots and Systems (IROS), 2020
project page (Coming soon...)/ arXiv (Coming soon...)

A perception and motion planning framework that considers the uncertainty in object poses to generate picking plans that minimizes the chance of collision and maximizes the likelihood of success.

Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive Hands
Bowen Wen, Chaitanya Mitash, Sruthi Soorian, Andrew Kimmel, Avishai Sintov and Kostas Bekris
IEEE International Conference on Robotics and Automation (ICRA), 2020
arXiv/ code

Simultaneous pose estimation of robot hand and the hand-held object via 3d pointset registration while considering physical consistency.

That and There: Judging the Intent of Pointing Actions with Robotic Arms
Malihe Alikhani, Baber Khalid, Rahul Shome, Chaitanya Mitash, Kostas Bekris, Matthew Stone
AAAI Conference on Artificial Intelligence, 2020
arXiv

Presents a set of interpretive principles for how a robotic arm can use pointing actions to communicate task information to people. The evaluation distinguishes two classes of pointing actions that arise in pick-and- place tasks: referential pointing (identifying objects) and locating pointing (identifying locations).

Scene-level Pose Estimation for Multiple Instances of Densely Packed Objects
Chaitanya Mitash, Bowen Wen, Kostas Bekris, and Abdeslam Boularias
Conference on Robot Learning (CoRL), 2019
arXiv / code

Introduces key machine learning operations for joint 6D pose estimation of multiple instances of objects in challenging scenarios by learning over just simulated data.

Towards Robust Product Packing with a Minimalistic End-Effector
Rahul Shome, Wei N. Tang, Changkyu Song, Chaitanya Mitash, Chris Kourtev, Jingjin Yu, Abdeslam Boularias, and Kostas Bekris
IEEE International Conference on Robotics and Automation (ICRA), 2019
project page / arXiv

Introduces manipulation primitives (toppling, adaptive pushing and fine-correction) to achieve robust bin packing with a simple vacuum-based end-effector. A robotic system is develeoped and large-scale experiments are performed to demonstrate its efficacy.

Learning Object Localization and 6D Pose Estimation from Simulation and Weakly Labeled Real Images
Jean-Philippe Mercier, Chaitanya Mitash, Philippe Giguere and Abdeslam Boularias
IEEE International Conference on Robotics and Automation (ICRA), 2019
arXiv

Sim2real adaptation of object detector via weakly labeled images and utilization of activation maps for 6d object pose estimation.

Robust 6D Object Pose Estimation with Stochastic Congruent Sets
Chaitanya Mitash, Abdeslam Boularias and Kostas Bekris
British Machine Vision Conference (BMVC), 2018
arXiv / code

A soft segmentation output can be used to guide sampling-based pointset registration to retrieve object poses.

Physics-based Scene-level Reasoning for Object Pose Estimation in Clutter
Chaitanya Mitash, Abdeslam Boularias and Kostas Bekris
International Journal of Robotics Research (IJRR), 2019
project page / arXiv

Integrates the self-supervised learning via physics simulation with online scene-level reasoning.

Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search
Chaitanya Mitash, Abdeslam Boularias and Kostas Bekris
IEEE International Conference on Robotics and Automation (ICRA), 2018
project page / arXiv

A combinatorial search algorithm to generate, evaluate and select physically-consistent scene hypothesis from independent object detections.

A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation
Chaitanya Mitash, Kostas Bekris and Abdeslam Boularias
IEEE International Conference on Intelligent Robots and Systems (IROS), 2017
project page / arXiv / code

Physically-realistic simulation allows better training for object detection in robotics setup and can bootstrap a self-learning process to automatically collect and label real images and improve the detector over time.

Workshop & Talks

Robotics Science and Systems (RSS) Pioneers, 2019 (Talk / Abstract)
4th International Workshop on Recovering 6D Object Pose, ECCV, 2018 (Poster)
Robotics for logistics in warehouses and environments shared with humans, IROS, 2018 (Abstract)
Warehouse Picking Automation Workshop, ICRA, 2017 (Abstract)
Northeast Robotics Colloquium (NERC), 2017 (Poster)
Service

(Reviewer) Conference on Neural Information Processing Systems/NeurIPS (2019, 2020)
(Reviewer) IEEE Robotics and Automation Letters/RA-L (2018, 2020)
(Reviewer) IEEE International Conference on Intelligent Robots and Systems/IROS (2017, 2018, 2019, 2020)
(Reviewer) IEEE International Conference on Robotics and Automation/ICRA (2018, 2019)
(Reviewer) Conference on Computer Vision and Pattern Recognition/CVPR 2019
(Meta-reviewer) Robotics Science and Systems (RSS) Pioneers 2020
(Assisted Review) Robotics Science and Systems (RSS) 2020
(Assisted Review) The Workshop on the Algorithmic Foundations of Robotics/WAFR 2020
(Assisted Review) Conference on Robot Learning/CoRL 2019



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