Area of research
Artificial Intelligence · Computer Vision and Pattern Recognition
Research interest
Research interests include Computer science, Task (project management), Motion planning, Robot, Artificial intelligence, and Probabilistic logic.
Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks
Fragmented Spatial Maps from Surprisal: State Abstraction and Efficient Planning
PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning
Learning to guide task and motion planning using score-space representation
Effect of Depth and Width on Local Minima in Deep Learning
Every Local Minimum Value Is the Global Minimum Value of Induced Model in Nonconvex Machine Learning
DSpace@MIT (Massachusetts Institute of Technology) 2019cited by 4position: last
Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing
FFRob: Leveraging symbolic planning for efficient task and motion planning
Sample-Based Methods for Factored Task and Motion Planning
Generalizing Over Uncertain Dynamics for Online Trajectory Generation
Policy search for multi-robot coordination under uncertainty
Learning to Rank for Synthesizing Planning Heuristics
FFRob: An Efficient Heuristic for Task and Motion Planning
Planning for decentralized control of multiple robots under uncertainty
Symbol acquisition for probabilistic high-level planning
DSpace@MIT (Massachusetts Institute of Technology) 2015cited by 40position: middle
Policy Search for Multi-Robot Coordination under Uncertainty
A constraint-based method for solving sequential manipulation planning problems
Planning with macro-actions in decentralized POMDPs
Planning for Decentralized Control of Multiple Robots Under Uncertainty
Integrated task and motion planning in belief space
A hierarchical approach to manipulation with diverse actions
Manipulation with Multiple Action Types
LQR-RRT*: Optimal sampling-based motion planning with automatically derived extension heuristics