Author name cluster

Yahav Avigal

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.

7 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 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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ICRA Conference 2023 Conference Paper

SGTM 2. 0: Autonomously Untangling Long Cables using Interactive Perception

Kaushik Shivakumar
Vainavi Viswanath
Anrui Gu
Yahav Avigal
Justin Kerr
Jeffrey Ichnowski
Richard Cheng
Thomas Kollar

Cables are commonplace in homes, hospitals, and industrial warehouses and are prone to tangling. This paper extends prior work on autonomously untangling long cables by introducing novel uncertainty quantification metrics and actions that interact with the cable to reduce perception uncertainty. We present Sliding and Grasping for Tangle Manipulation 2. 0 (SGTM 2. 0), a system that autonomously untangles cables approximately 3 meters in length with a bilateral robot using estimates of uncertainty at each step to inform actions. By interactively reducing uncertainty, SGTM 2. 0 significantly reduces run-time. Physical experiments with 84 trials suggest that SGTM $2. 0$ can achieve 83% untangling success on cables with 1 or 2 overhand and figure-8 knots, and 70% termination detection success across these configurations, outperforming SGTM 1. 0 by 43% in untangling accuracy and 200% in completion time. Supplementary material, visualizations, and videos can be found at sites.google.com/view/sgtm2.

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ICRA Conference 2022 Conference Paper

GOMP-FIT: Grasp-Optimized Motion Planning for Fast Inertial Transport

Jeffrey Ichnowski
Yahav Avigal
Yi Liu
Ken Goldberg

High-speed motions in pick-and-place operations are critical to making robots cost-effective in many automation scenarios, from warehouses and manufacturing to hospitals and homes. However, motions can be too fast-such as when the object being transported has an open-top, is fragile, or both. One way to avoid spills or damage, is to move the arm slowly. We propose an alternative: Grasp-Optimized Motion Planning for Fast Inertial Transport (GOMP-FIT), a time-optimizing motion planner based on our prior work, that includes con-straints based on accelerations at the robot end-effector. With GOMP-FIT, a robot can perform high-speed motions that avoid obstacles and use inertial forces to its advantage. In experiments transporting open-top containers with varying tilt tolerances, whereas GOMP computes sub-second motions that spill up to 90 % of the contents during transport, GOMP-FIT generates motions that spill 0 % of contents while being slowed by as little as 0 % when there are few obstacles, 30 % when there are high obstacles and 45-degree tolerances, and 50 % when there 15-degree tolerances and few obstacles. Videos and more at: https://berkeleyautomation.github.io/gomp-fit/.

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IROS Conference 2022 Conference Paper

SpeedFolding: Learning Efficient Bimanual Folding of Garments

Yahav Avigal
Lars Berscheid
Tamim Asfour
Torsten Kröger
Ken Goldberg

Folding garments reliably and efficiently is a long standing challenge in robotic manipulation due to the complex dynamics and high dimensional configuration space of garments. An intuitive approach is to initially manipulate the garment to a canonical smooth configuration before folding. In this work, we develop SpeedFolding, a reliable and efficient bimanual system, which given user-defined instructions as folding lines, manipulates an initially crumpled garment to (1) a smoothed and (2) a folded configuration. Our primary contribution is a novel neural network architecture that is able to predict pairs of gripper poses to parameterize a diverse set of bimanual action primitives. After learning from 4300 human- annotated and self-supervised actions, the robot is able to fold garments from a random initial configuration in under 120 s on average with a success rate of 93 %. Real-world experiments show that the system is able to generalize to unseen garments of different color, shape, and stiffness. While prior work achieved 3–6 Folds Per Hour (FPH), SpeedFolding achieves 30–40 FPH. See https://pantor.github.io/speedfolding for code, videos, and datasets.

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ICRA Conference 2021 Conference Paper

Learning Seed Placements and Automation Policies for Polyculture Farming with Companion Plants

Yahav Avigal
Anna Deza
William Wong
Sebastian Oehme
Mark Presten
Mark Theis
Jackson Chui
Paul Shao

Polyculture farming is a sustainable farming technique based on synergistic interactions between differing plant types that make them more resistant to diseases and pests and better able to retain water. Reduced uniformity can reduce use of pesticides, fertilizer, and water, but is more labor intensive and more challenging to automate. We describe a scaled physical testbed (1. 5m×3. 0m) that uses a high resolution camera and soil sensors to monitor polyculture plants to facilitate tuning of plant growth, companion effects, and irrigation parameters for a first-order garden simulator. We use this simulator to develop a novel seed placement algorithm that increases coverage and diversity, and a learned pruning policy. In simulation experiments, the seed placement algorithm yields 60% more coverage and 10% more diversity than random seed placement and the learned pruning policy runs 1000X faster than a procedural lookahead policy to achieve high leaf coverage and plant diversity on adversarial gardens that include plant species with diverse growth rates. These models and policies provide the groundwork for a fully-automated system under development. Code, datasets and supplementary material can be found at https://github.com/BerkeleyAutomation/AlphaGarden/.

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ICRA Conference 2020 Conference Paper

Dex-Net AR: Distributed Deep Grasp Planning Using a Commodity Cellphone and Augmented Reality App

Harry Zhang
Jeffrey Ichnowski
Yahav Avigal
Joseph E. Gonzalez
Ion Stoica
Ken Goldberg

Consumer demand for augmented reality (AR) in mobile phone applications, such as the Apple ARKit. Such applications have potential to expand access to robot grasp planning systems such as Dex-Net. AR apps use structure from motion methods to compute a point cloud from a sequence of RGB images taken by the camera as it is moved around an object. However, the resulting point clouds are often noisy due to estimation errors. We present a distributed pipeline, Dex-Net AR, that allows point clouds to be uploaded to a server in our lab, cleaned, and evaluated by Dex-Net grasp planner to generate a grasp axis that is returned and displayed as an overlay on the object. We implement Dex-Net AR using the iPhone and ARKit and compare results with those generated with high-performance depth sensors. The success rates with AR on harder adversarial objects are higher than traditional depth images. The server URL is https://sites.google.com/berkeley.edu/dex-net-ar/home.

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