Unifying Perception, Estimation and Action for Mobile Manipulation via Belief Space Planning Leslie Pack Kaelbling and Tom´as Lozano-P´erez Abstract— In this paper, we describe an integrated strategy for planning, pe

Source URL: people.csail.mit.edu

• Motion planning
• Theoretical computer science
• Pose
• Occupancy grid mapping
• Robot
• Partially observable Markov decision process
• Artificial intelligence
• Fluent
• Causality
• Computer vision
• Statistics
• Applied mathematics

Robust Belief-Based Execution of Manipulation Programs Kaijen Hsiao, Tom´ as Lozano-P´erez, and Leslie Pack Kaelbling Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, {kjh

Source URL: people.csail.mit.edu

• Stochastic control
• Dynamical system
• Motion planning
• Robot
• Kinematics
• Normal distribution
• Mobile robot
• Statistics
• Dynamic programming
• Partially observable Markov decision process

Artificial Intelligence[removed]–134 Planning and acting in partially observable stochastic domains Leslie Pack Kaelbling a,∗,1,2 , Michael L. Littman b,3 , Anthony R. Cassandra c,1

Source URL: people.csail.mit.edu

• Stochastic control
• Markov models
• Partially observable Markov decision process
• Markov decision process
• Reinforcement learning
• Automated planning and scheduling
• Markov chain
• Kalman filter
• Statistics
• Markov processes
• Dynamic programming

Non-Gaussian Belief Space Planning: Correctness and Complexity Robert Platt, Leslie Kaelbling, Tomas Lozano-Perez, Russ Tedrake Abstract— We consider the partially observable control problem where it is potentially nec

Source URL: people.csail.mit.edu

• Estimation theory
• Robot control
• Probability and statistics
• Normal distribution
• Particle filter
• Pi
• Statistics
• Mathematical analysis
• Monte Carlo methods

Approximate Planning in POMDPs with Macro-Actions Georgios Theocharous MIT AI Lab 200 Technology Square Cambridge, MA 02139

Source URL: people.csail.mit.edu

• Stochastic control
• Markov models
• Partially observable Markov decision process
• Models of computation
• Markov decision process
• Automated planning and scheduling
• Reinforcement learning
• Algorithm
• Q-learning
• Statistics
• Markov processes
• Dynamic programming

Collision Avoidance for Unmanned Aircraft using Markov Decision Processes∗ Selim Temizer†, Mykel J. Kochenderfer‡, Leslie P. Kaelbling§, Tom´as Lozano-P´erez¶, and James K. Kuchark Before unmanned aircraft can

Source URL: people.csail.mit.edu

• Warning systems
• Dynamic programming
• Stochastic control
• Safety
• Control theory
• Traffic collision avoidance system
• Collision avoidance
• Partially observable Markov decision process
• Airborne collision avoidance system
• Statistics
• Avionics
• Aircraft collision avoidance systems

Cosmic Microwave Background

Source URL: www.astro.ucla.edu

• Space
• Cosmic microwave background radiation
• Inflation
• Horizon problem
• Observable universe
• Universe
• Edward L. Wright
• Dark matter
• Cosmic Background Explorer
• Physics
• Physical cosmology
• Astronomy

Efficient planning in non-Gaussian belief spaces and its application to robot grasping Robert Platt, Leslie Kaelbling, Tomas Lozano-Perez, and Russ Tedrake Abstract The limited nature of robot sensors make many important

Source URL: people.csail.mit.edu

• Robot control
• Cybernetics
• Linear filters
• Markov models
• Kalman filter
• Particle filter
• Partially observable Markov decision process
• Normal distribution
• Recursive Bayesian estimation
• Statistics
• Control theory
• Estimation theory

Object: Observable Universe 300,000 years after the Big Bang Distance from Earth: 13.6 billion light-years

Source URL: naturalhistory.si.edu

Task-Driven Tactile Exploration Kaijen Hsiao Willow Garage Menlo Park, CA Email: [removed]

Source URL: people.csail.mit.edu

• Formal languages
• Dynamic programming
• Partially observable Markov decision process
• Stochastic control
• Object recognition
• Interpretation
• Function
• Motion planning
• Limit
• Mathematics
• Statistics
• Logic