Search Results for author: Toki Migimatsu

Found 6 papers, 1 papers with code

Category-Independent Articulated Object Tracking with Factor Graphs

no code implementations7 May 2022 Nick Heppert, Toki Migimatsu, Brent Yi, Claire Chen, Jeannette Bohg

Robots deployed in human-centric environments may need to manipulate a diverse range of articulated objects, such as doors, dishwashers, and cabinets.

Object Tracking

OmniHang: Learning to Hang Arbitrary Objects using Contact Point Correspondences and Neural Collision Estimation

1 code implementation26 Mar 2021 Yifan You, Lin Shao, Toki Migimatsu, Jeannette Bohg

In this paper, we propose a system that takes partial point clouds of an object and a supporting item as input and learns to decide where and how to hang the object stably.

Object-Centric Task and Motion Planning in Dynamic Environments

no code implementations12 Nov 2019 Toki Migimatsu, Jeannette Bohg

We address the problem of applying Task and Motion Planning (TAMP) in real world environments.

Motion Planning

Learning to Scaffold the Development of Robotic Manipulation Skills

no code implementations3 Nov 2019 Lin Shao, Toki Migimatsu, Jeannette Bohg

To combat these factors and achieve more robust manipulation, humans actively exploit contact constraints in the environment.

Driverseat: Crowdstrapping Learning Tasks for Autonomous Driving

no code implementations7 Dec 2015 Pranav Rajpurkar, Toki Migimatsu, Jeff Kiske, Royce Cheng-Yue, Sameep Tandon, Tao Wang, Andrew Ng

While emerging deep-learning systems have outclassed knowledge-based approaches in many tasks, their application to detection tasks for autonomous technologies remains an open field for scientific exploration.

Autonomous Driving Lane Detection

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