Ritvik Singh

Robotics | Simulation | Perception

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About Me

I am a research engineer at NVIDIA currently working on robotics and simulation with Ankur Handa, Nathan Ratliff, and Karl Van Wyk


My interests lie at the intersection of perception and control in order to build intelligent robot systems that can interact with the physical world. Currently, my research focuses on leveraging learning-based methods to achieve human-like dexterous manipulation in unstructured environments.


Previously, I studied Engineering Science at the University of Toronto where I collaborated with Prof. Florian Shkurti and Prof. Animesh Garg.

Publications

Visuomotor Policies to Grasp Anything with Dexterous Hands

Authors: Ritvik Singh, Arthur Allshire, Ankur Handa, Nathan Ratliff, Karl Van Wyk

Preprint

One of the most important yet challenging skills for robots is dexterous multi-fingered grasping of a diverse range of objects. Much of the prior work is limited by the speed, dexterity, or reliance on depth maps. In this paper, we introduce DextrAH-RGB, a system that can perform dexterous arm-hand grasping end2end from stereo RGB input. We train a teacher policy in simulation through reinforcement learning that acts on a geometric fabric action space to ensure reactivity and safety. We then distill this teacher into an RGB-based student in simulation. To our knowledge, this is the first work that is able to demonstrate robust sim2real transfer of an end2end RGB-based policy for a complex, dynamic, contact-rich tasks such as dexterous grasping. Our policies are also able to generalize to grasping novel objects with unseen geometry, texture, or lighting conditions during training.

Publication 1

Synthetica: Large Scale Synthetic Data Generation for Robot Perception

Authors: Ritvik Singh, Jingzhou Liu, Karl Van Wyk, Yu-Wei Chao, Jean-Francois Lafleche, Florian Shkurti, Nathan Ratliff, Ankur Handa

Arxiv

We present Synthetica, a method for large-scale synthetic data generation for training robust state estimators. This paper focuses on the task of object detection, an important problem which can serve as the front-end for most state estimation problems, such as pose estimation. Leveraging data from a photorealistic ray-tracing renderer, we scale up data generation, generating 2.7 million images, to train highly accurate real-time detection transformers. We demonstrate state-of-the-art performance on the task of object detection while having detectors that run at 50--100Hz which is 9 times faster than the prior SOTA.

DeXtreme: Transfer of Agile In-Hand Manipulation from Simulation to Reality

Authors: Ankur Handa*, Arthur Allshire*, Viktor Makoviychuk*, Aleksei Petrenko*, Ritvik Singh*, Jingzhou Liu*, Denys Makoviichuk, Karl Van Wyk, Alexander Zhurkevich, Balakumar Sundaralingam, Yashraj Narang, Jean-Francois Lafleche, Dieter Fox, Gavriel State

ICRA 2023

This paper presents a novel approach for transferring agile in-hand manipulation from simulation to reality. We leverage deep reinforcement learning and advanced simulation techniques to train a dexterous hand in a virtual environment and successfully transfer the learned policy to a physical robot. The results demonstrate significant improvements in manipulation accuracy and robustness in real-world settings.

Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments

Authors: Mayank Mittal, Calvin Yu, Qinxi Yu, Jingzhou Liu, Nikita Rudin, David Hoeller, Jia Lin Yuan, Ritvik Singh, Yunrong Guo Hammad Mazhar, Ajay Mandlekar, Buck Babich, Gavriel State, Marco Hutter, Animesh Garg

IROS 2023 | RA-L

We present ORBIT, a unified and modular framework for robot learning powered by NVIDIA Isaac Sim. It offers a modular design to easily and efficiently create robotic environments with photo-realistic scenes and fast and accurate rigid and deformable body simulation. ORBIT allows training reinforcement learning policies and collecting large demonstration datasets from hand-crafted or expert solutions in a matter of minutes by leveraging GPU-based parallelization. With this framework, we aim to support various research areas, including representation learning, reinforcement learning, imitation learning, and task and motion planning.

Publication 1

Fast-Grasp'D: Dexterous Multi-finger Grasp Generation Through Differentiable Simulation

Authors: Dylan Turpin, Tao Zhong, Shutong Zhang, Guanglei Zhu, Jingzhou Liu, Ritvik Singh, Eric Heiden, Miles Macklin, Stavros Tsogkas, Sven Dickinson, Animesh Garg

IROS 2023 | RA-L

Multi-finger grasping relies on high quality training data, which is hard to obtain: human data is hard to transfer and synthetic data relies on simplifying assumptions that reduce grasp quality. By making grasp simulation differentiable, and contact dynamics amenable to gradient-based optimization, we accelerate the search for high-quality grasps with fewer limiting assumptions. We present Grasp'D-1M: a large-scale dataset for multi-finger robotic grasping, synthesized with Fast- Grasp'D, a novel differentiable grasping simulator.