Shivin Dass

Hi, I am a PhD student at University of Texas, Austin where I work with Prof. Roberto Martín-Martín at the RobIn lab on robotics, imitation learning and reinforcement learning.

I completed my masters at University of Southern California (USC) where I worked on the problem of large scale robot data collection at the CLVR Lab with Prof. Joseph Lim. I completed my Bachelors in Technology in CSE from IIIT Delhi.

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Research

My research goal is to develop deep learning algorithms to not only advance robot intelligence but also to deploy them in real-world robotic applications. To this end, my current research focuses on improving robot data collection and utilizing the datasets to learn robust and generalizable low-level policies.

Publications
TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation
Shivin Dass, Wensi Ai, Yuqian Jiang, Samik Singh, Jiaheng Hu, Ruohan Zhang, Peter Stone, Ben Abbatematteo, Roberto Martín-Martín
ArXiV, 2024
project page / arXiv / code

TeleMoMa is a teleoperation toolkit that enables intuitive teleoperation of high-DoF mobile manipulators. TeleMoMa supports several teleoperation interfaces such as vision, VR, mobile phones and more. TeleMoMa is not only versatile, allowing easy plug-and-play teleoperation of any mobile manipulator in general, but is also modular, enabling mixing-and-matching various teleoperation interfaces to provide the most effective teleoperation experience.

DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
ArXiV, 2024
project page / arXiv / data visualizer

We introduce DROID, the most diverse robot manipulation dataset to date. It contains 76k demonstration trajectories or 350 hours of interaction data, collected across 564 scenes and 84 tasks by 50 data collectors in North America, Asia, and Europe over the course of 12 months. We demonstrate that training with DROID leads to policies with higher performance and improved generalization ability. We open source the full dataset, policy learning code, and a detailed guide for reproducing our robot hardware setup.

Model-Based Runtime Monitoring with Interactive Imitation Learning
Huihan Liu, Shivin Dass, Roberto Martín-Martín, Yuke Zhu
IEEE International Conference on Robotics and Automation (ICRA), 2024
project page / arXiv / code

We introduce a runtime monitoring system that utilizes human interventions from on-the-job data to learn to classify dangerous states. Our model-based design enables us to rollout the future and preemptively ask for help from the human supervisor. Our method outperforms the baselines, with 23% and 40% higher success rates in simulation and on physical hardware, respectively.

Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Open X-Embodiment Collaboration
IEEE International Conference on Robotics and Automation (ICRA), 2024
project page / arXiv / code

Introducing Open X-Embodiment Dataset, the largest robot learning dataset to date, with 1M+ real robot trajectories. By training large transformer based policies (RT-1-X, RT-2-X) on this dataset, we find that co-training over multiple embodiments substantially improves performance of the policies.
Role: As an early collaborator, I contributed the USC Jaco Play dataset and conducted evaluations, that led to useful insights about large scale co-training.

PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection
Shivin Dass*, Karl Pertsch*, Hejia Zhang, Youngwoon Lee, Joseph J. Lim, Stefanos Nikolaidis
Robotics: Science and Systems (RSS), 2023
project page / arXiv / code

We enable scalable robot data collection by assisting human teleoperators with a learned policy. Our approach estimates its uncertainty over future actions to determine when to request user input. In real world user studies we demonstrate that our system enables more efficient teleoperation with reduced mental load and up to four robots in parallel.


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