409 citations · 725 across the 12 of their papers we have counts for
12 papers
COAST: Constraints and Streams for Task and Motion Planning
Brandon Vu, Toki Migimatsu, Jeannette Bohg
Task and Motion Planning (TAMP) algorithms solve long-horizon robotics tasks by integrating task planning with motion planning; the task planner proposes a sequence of actions towa…
Text2Motion: From Natural Language Instructions to Feasible Plans
Kevin Lin, Christopher Agia, Toki Migimatsu +2
We propose Text2Motion, a language-based planning framework enabling robots to solve sequential manipulation tasks that require long-horizon reasoning. Given a natural language ins…
Active Task Randomization: Learning Robust Skills via Unsupervised Generation of Diverse and Feasible Tasks
Kuan Fang, Toki Migimatsu, Ajay Mandlekar +2
Solving real-world manipulation tasks requires robots to have a repertoire of skills applicable to a wide range of circumstances. When using learning-based methods to acquire such…
STAP: Sequencing Task-Agnostic Policies
Christopher Agia, Toki Migimatsu, Jiajun Wu +1
Advances in robotic skill acquisition have made it possible to build general-purpose libraries of learned skills for downstream manipulation tasks. However, naively executing these…
Category-Independent Articulated Object Tracking with Factor Graphs
Nick Heppert, Toki Migimatsu, Brent Yi +2
Robots deployed in human-centric environments may need to manipulate a diverse range of articulated objects, such as doors, dishwashers, and cabinets. Articulated objects often com…
Symbolic State Estimation with Predicates for Contact-Rich Manipulation Tasks
Toki Migimatsu, Wenzhao Lian, Jeannette Bohg +1
Manipulation tasks often require a robot to adjust its sensorimotor skills based on the state it finds itself in. Taking peg-in-hole as an example: once the peg is aligned with the…
Grounding Predicates through Actions
Toki Migimatsu, Jeannette Bohg
Symbols representing abstract states such as "dish in dishwasher" or "cup on table" allow robots to reason over long horizons by hiding details unnecessary for high-level planning.…
OmniHang: Learning to Hang Arbitrary Objects using Contact Point Correspondences and Neural Collision Estimation
Yifan You, Lin Shao, Toki Migimatsu +1
In this paper, we explore whether a robot can learn to hang arbitrary objects onto a diverse set of supporting items such as racks or hooks. Endowing robots with such an ability ha…
Object-Centric Task and Motion Planning in Dynamic Environments
Toki Migimatsu, Jeannette Bohg
We address the problem of applying Task and Motion Planning (TAMP) in real world environments. TAMP combines symbolic and geometric reasoning to produce sequential manipulation pla…
Learning to Scaffold the Development of Robotic Manipulation Skills
Lin Shao, Toki Migimatsu, Jeannette Bohg
Learning contact-rich, robotic manipulation skills is a challenging problem due to the high-dimensionality of the state and action space as well as uncertainty from noisy sensors a…
Driverseat: Crowdstrapping Learning Tasks for Autonomous Driving
Pranav Rajpurkar, Toki Migimatsu, Jeff Kiske +4
While emerging deep-learning systems have outclassed knowledge-based approaches in many tasks, their application to detection tasks for autonomous technologies remains an open fiel…
An Empirical Evaluation of Deep Learning on Highway Driving
Brody Huval, Tao Wang, Sameep Tandon +10
Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical ev…