no code implementations • 7 Apr 2015 • Brody Huval, Tao Wang, Sameep Tandon, Jeff Kiske, Will Song, Joel Pazhayampallil, Mykhaylo Andriluka, Pranav Rajpurkar, Toki Migimatsu, Royce Cheng-Yue, Fernando Mujica, Adam Coates, Andrew Y. Ng
We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection.
Ranked #2 on Lane Detection on Caltech Lanes Cordova
no code implementations • 7 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.
no code implementations • 3 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.
no code implementations • 12 Nov 2019 • Toki Migimatsu, Jeannette Bohg
We address the problem of applying Task and Motion Planning (TAMP) in real world environments.
1 code implementation • 26 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.
no code implementations • 7 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.
no code implementations • 21 Oct 2022 • Christopher Agia, Toki Migimatsu, Jiajun Wu, Jeannette Bohg
We further demonstrate how STAP can be used for task and motion planning by estimating the geometric feasibility of skill sequences provided by a task planner.
no code implementations • 11 Nov 2022 • Kuan Fang, Toki Migimatsu, Ajay Mandlekar, Li Fei-Fei, Jeannette Bohg
ATR selects suitable tasks, which consist of an initial environment state and manipulation goal, for learning robust skills by balancing the diversity and feasibility of the tasks.