Robot Grasping in Clutter: Using a Hierarchy of Supervisors for Learning from Demonstrations Michael Laskey1 , Jonathan Lee1 , Caleb Chuck1 , David Gealy1 , Wesley Hsieh1 , Florian T. Pokorny1 , Anca D. Dragan1 , and Ken

Source URL: goldberg.berkeley.edu

• Machine learning
• Robot learning
• Artificial neural network
• Deep learning
• Motion planning
• Robotics
• Supervisor
• Apprenticeship learning
• Clutter
• Robot
• Reinforcement learning

RAMANUJAN’S MOCK THETA FUNCTIONS MICHAEL GRIFFIN, KEN ONO, AND LARRY ROLEN Abstract. In his famous deathbed letter, Ramanujan introduced the notion of a mock theta function, and he offered some alleged examples. Recent

Source URL: web.math.princeton.edu

Proof of the Umbral Moonshine Conjecture John F. R. Duncan, Michael J. Griffin and Ken Ono 2015 October 13 Abstract The Umbral Moonshine Conjectures assert that there are infinite-dimensional graded modules, for prescrib

Source URL: web.math.princeton.edu

MOONSHINE JOHN F. R. DUNCAN, MICHAEL J. GRIFFIN AND KEN ONO Abstract. Monstrous moonshine relates distinguished modular functions to the representation theory of the Monster M. The celebrated observations that (*) 1 = 1

Source URL: web.math.princeton.edu

The International Journal of Robotics Research http://ijr.sagepub.com Motion Planning Under Uncertainty for Image-guided Medical Needle Steering Ron Alterovitz, Michael Branicky and Ken Goldberg

Source URL: goldberg.berkeley.edu

• Medicine
• Clinical medicine
• Motion planning
• Theoretical computer science
• Biopsy
• Hypodermic needle
• Magnetism
• Needle
• Stereotactic surgery
• Shortest path problem

WEIERSTRASS MOCK MODULAR FORMS AND ELLIPTIC CURVES CLAUDIA ALFES, MICHAEL GRIFFIN, KEN ONO, AND LARRY ROLEN Abstract. Mock modular forms, which give the theoretical framework for Ramanujan’s enigmatic mock theta functi

Source URL: web.math.princeton.edu

RAMANUJAN’S MOCK THETA FUNCTIONS MICHAEL GRIFFIN, KEN ONO, AND LARRY ROLEN Abstract. In his famous deathbed letter, Ramanujan introduced the notion of a mock theta function, and he offered some alleged examples. Recent

Source URL: www.mathcs.emory.edu

Multi-Armed Bandit Models for 2D Grasp Planning with Uncertainty Michael Laskey1 , Jeff Mahler1 , Zoe McCarthy1 , Florian T. Pokorny1 , Sachin Patil1 , Jur van den Berg4 , Danica Kragic3 , Pieter Abbeel1 , Ken Goldberg2

Source URL: goldberg.berkeley.edu

• Statistics
• Probability
• Child development
• Grasp
• Reinforcement learning
• Gittins index
• Gaussian process
• Sensitivity analysis
• Monte Carlo integration
• Probability distribution
• Monte Carlo method
• Sampling

A FRAMEWORK OF ROGERS–RAMANUJAN IDENTITIES AND THEIR ARITHMETIC PROPERTIES MICHAEL J. GRIFFIN, KEN ONO, AND S. OLE WARNAAR In memory of Basil Gordon and Alain Lascoux Abstract. The two Rogers–Ramanujan q-series ∞

Source URL: web.math.princeton.edu

Scaling up Gaussian Belief Space Planning through Covariance-Free Trajectory Optimization and Automatic Differentiation Sachin Patil, Gregory Kahn, Michael Laskey, John Schulman, Ken Goldberg, Pieter Abbeel University of

Source URL: goldberg.berkeley.edu

• Mathematical optimization
• Systems theory
• Engineering
• Systems science
• Control theory
• Robot control
• Operations research
• Optimal control
• Trajectory optimization
• Kalman filter
• Model predictive control
• Quadratic programming