Author name cluster

Karthik Dharmarajan

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

IROS Conference 2024 Conference Paper

BOMP: Bin-Optimized Motion Planning

Zachary Tam
Karthik Dharmarajan
Tianshuang Qiu
Yahav Avigal
Jeffrey Ichnowski
Ken Goldberg

In logistics, the ability to quickly compute and execute pick-and-place motions from bins is critical to increasing productivity. We present Bin-Optimized Motion Planning (BOMP), a motion planning framework that plans arm motions for a six-axis industrial robot with a long-nosed suction tool to remove boxes from deep bins. BOMP considers robot arm kinematics, actuation limits, the dimensions of a grasped box, and a varying height map of a bin environment to rapidly generate time-optimized, jerk-limited, and collision-free trajectories. The optimization is warm-started using a deep neural network trained offline in simulation with 25, 000 scenes and corresponding trajectories. Experiments with 96 simulated and 15 physical environments suggest that BOMP generates collision-free trajectories that are up to 58% faster than baseline sampling-based planners and up to 36% faster than an industry-standard Up-Over-Down algorithm, which has an extremely low 15% success rate in this context. BOMP also generates jerk-limited trajectories while baselines do not. Website: https://sites.google.com/berkeley.edu/bomp.

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ICRA Conference 2024 Conference Paper

Orbit-Surgical: An Open-Simulation Framework for Learning Surgical Augmented Dexterity

Qinxi Yu
Masoud Moghani
Karthik Dharmarajan
Vincent Schorp
William Chung-Ho Panitch
Jingzhou Liu
Kush Hari
Huang Huang

Physics-based simulations have accelerated progress in robot learning for driving, manipulation, and locomotion. Yet, a fast, accurate, and robust surgical simulation environment remains a challenge. In this paper, we present Orbit-Surgical, a physics-based surgical robot simulation framework with photorealistic rendering in NVIDIA Omniverse. We provide 14 benchmark surgical tasks for the da Vinci Research Kit (dVRK) and Smart Tissue Autonomous Robot (STAR) which represent common subtasks in surgical training. Orbit-Surgical leverages GPU parallelization to train reinforcement learning and imitation learning algorithms to facilitate study of robot learning to augment human surgical skills. Orbit-Surgical also facilitates realistic synthetic data generation for active perception tasks. We demonstrate Orbit-Surgical sim-to-real transfer of learned policies onto a physical dVRK robot. Project website: orbit-surgical. github.io

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ICRA Conference 2023 Conference Paper

Automating Vascular Shunt Insertion with the dVRK Surgical Robot

Karthik Dharmarajan
William Chung-Ho Panitch
Muyan Jiang
Kishore Srinivas
Baiyu Shi
Yahav Avigal
Huang Huang
Thomas Low

Vascular shunt insertion is a fundamental surgical procedure used to temporarily restore blood flow to tissues. It is often performed in the field after major trauma. We formulate a problem of automated vascular shunt insertion and propose a pipeline to perform Automated Vascular Shunt Insertion (AVSI) using a da Vinci Research Kit. The pipeline uses a learned visual model to estimate the locus of the vessel rim, plans a grasp on the rim, and moves to grasp at that point. The first robot gripper then pulls the rim to stretch open the vessel with a dilation motion. The second robot gripper then proceeds to insert a shunt into the vessel phantom (a model of the blood vessel) with a chamfer tilt followed by a screw motion. Results suggest that AVSI achieves a high success rate even with tight tolerances and varying vessel orientations up to 30°. Supplementary material, dataset, videos, and visualizations can be found at https://sites.google.com/berkeley.edu/autolab-avsi.

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IROS Conference 2023 Conference Paper

FogROS2-SGC: A ROS2 Cloud Robotics Platform for Secure Global Connectivity

Kaiyuan Chen 0001
Ryan Hoque
Karthik Dharmarajan
Edith LLontop
Simeon Adebola
Jeffrey Ichnowski
John Kubiatowicz
Ken Goldberg

The Robot Operating System (ROS2) is the most widely used software platform for building robotics applications. FogROS2 extends ROS2 to allow robots to access cloud computing on demand. We introduce FogROS2-SGC, an extension of FogROS2 that can effectively connect robot systems across different physical locations, networks, and Data Distribution Services (DDS). With globally unique and location-independent identifiers, FogROS2-SGC can securely and efficiently route data between robotics components around the globe. FogROS2-SGC is agnostic to the ROS2 distribution and configuration, is compatible with non-ROS2 software, and seamlessly extends existing ROS2 applications without any code modification. We evaluate FogROS2-SGC with 4 robots and compute nodes that are 3600 km apart. Experiments suggest FogROS2-SGC is 19x faster than rosbridge (a ROS2 package with comparable features, but lacking security). Videos and code are available on the website https://sites.google.com/view/fogros2-sgc.

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ICRA Conference 2023 Conference Paper

FogROS2: An Adaptive Platform for Cloud and Fog Robotics Using ROS 2

Jeffrey Ichnowski
Kaiyuan Chen 0001
Karthik Dharmarajan
Simeon Adebola
Michael Danielczuk
Víctor Mayoral Vilches
Nikhil Jha
Hugo Zhan

Mobility, power, and price points often dictate that robots do not have sufficient computing power on board to run contemporary robot algorithms at desired rates. Cloud computing providers such as AWS, GCP, and Azure offer immense computing power and increasingly low latency on demand, but tapping into that power from a robot is non-trivial. We present FogROS2, an open-source platform to facilitate cloud and fog robotics that is included in the Robot Operating System 2 (ROS 2) distribution. FogROS2 is distinct from its predecessor FogROS1 in 9 ways, including lower latency, overhead, and startup times; improved usability, and additional automation, such as region and computer type selection. Additionally, FogROS2 gains performance, timing, and additional improvements associated with ROS 2. In common robot applications, FogROS2 reduces SLAM latency by 50 %, reduces grasp planning time from 14 s to 1. 2 s, and speeds up motion planning 45x. When compared to FogROS1, FogROS2 reduces network utilization by up to 3. 8x, improves startup time by 63 %, and network round-trip latency by 97 % for images using video compression. The source code, examples, and documentation for FogROS2 are available at https://github.com/BerkeleyAutomation/FogROS2, and is available through the official ROS 2 repository at https://index.ros.org/p/FogROS2/.

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