Vainavi Viswanath — accepted AI-conference papers

2023

HANDLOOM: Learned Tracing of One-Dimensional Objects for Inspection and Manipulation

Vainavi Viswanath, Kaushik Shivakumar, Mallika Parulekar, Jainil Ajmera, Justin Kerr, Jeffrey Ichnowski +3

CoRL 2023oral

Tracing – estimating the spatial state of – long deformable linear objects such as cables, threads, hoses, or ropes, is useful for a broad range of tasks in homes, retail, factories, construction, transportation, and healthcare. For long deformable linear objects (DLOs or simply cables) with many (o…

Cited by 6 Source ↗code ↗Scholar ↗

2023

SGTM 2.0: Autonomously Untangling Long Cables using Interactive Perception

Kaushik Shivakumar, Vainavi Viswanath, Anrui Gu, Yahav Avigal, Justin Kerr, Jeffrey Ichnowski +3

ICRA 2023poster

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…

Cited by 20 Source ↗Scholar ↗

2022

Autonomously Untangling Long Cables

Vainavi Viswanath

RSS 2022poster

Cables are ubiquitous in many settings, but often become tangled. Cables are prone to self-occlusions and knots making them difficult to perceive and manipulate. This challenge is exacerbated as cable length increases: long cables require slack management and new primitives to facilitate observabili…

Cited by 26 Source ↗PDF ↗code ↗Scholar ↗

2021

Disentangling Dense Multi-Cable Knots

Vainavi Viswanath, Jennifer Grannen, Priya Sundaresan, Brijen Thananjeyan, Ashwin Balakrishna, Ellen Novoseller +4

IROS 2021poster

Disentangling two or more cables often requires many steps to remove crossings between and within cables. We formalize the problem of disentangling multiple cables and present an algorithm, Iterative Reduction Of Non-planar Multiple cAble kNots (IRON-MAN), that outputs robot actions to remove crossi…

Cited by 26 Source ↗Scholar ↗

2020

Untangling Dense Knots by Learning Task-Relevant Keypoints

Jennifer Grannen, Priya Sundaresan, Brijen Thananjeyan, Jeffrey Ichnowski, Ashwin Balakrishna, Vainavi Viswanath +3

CoRL 2020

Untangling ropes, wires, and cables is a challenging task for robots due to the high-dimensional configuration space, visual homogeneity, self-occlusions, and complex dynamics. We consider dense (tight) knots that lack space between self-intersections and present an iterative approach that uses lear

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗