Kuipers, Benjamin


Object Manipulation Learning by Imitation

arXiv.org Artificial Intelligence

We aim to enable robot to learn object manipulation by imitation. Given external observations of demonstrations on object manipulations, we believe that two underlying problems to address in learning by imitation is 1) segment a given demonstration into skills that can be individually learned and reused, and 2) formulate the correct RL (Reinforcement Learning) problem that only considers the relevant aspects of each skill so that the policy for each skill can be effectively learned. Previous works made certain progress in this direction, but none has taken private information into account. The public information is the information that is available in the external observations of demonstration, and the private information is the information that are only available to the agent that executes the actions, such as tactile sensations. Our contribution is that we provide a method for the robot to automatically segment the demonstration of object manipulations into multiple skills, and formulate the correct RL problem for each skill, and automatically decide whether the private information is an important aspect of each skill based on interaction with the world. Our experiment shows that our robot learns to pick up a block, and stack it onto another block by imitating an observed demonstration. The evaluation is based on 1) whether the demonstration is reasonably segmented, 2) whether the correct RL problems are formulated, 3) and whether a good policy is learned.



Remembering Marvin Minsky

AI Magazine

Marvin Minsky, one of the pioneers of artificial intelligence and a renowned mathematicial and computer scientist, died on Sunday, 24 January 2016 of a cerebral hemmorhage. In this article, AI scientists Kenneth D. Forbus (Northwestern University), Benjamin Kuipers (University of Michigan), and Henry Lieberman (Massachusetts Institute of Technology) recall their interactions with Minksy and briefly recount the impact he had on their lives and their research. A remembrance of Marvin Minsky was held at the AAAI Spring Symposium at Stanford University on March 22. Video remembrances of Minsky by Danny Bobrow, Benjamin Kuipers, Ray Kurzweil, Richard Waldinger, and others can be on the sentient webpage1 or on youtube.com.


Remembering Marvin Minsky

AI Magazine

Marvin Minsky, one of the pioneers of artificial intelligence and a renowned mathematicial and computer scientist, died on Sunday, 24 January 2016 of a cerebral hemmorhage. He was 88. In this article, AI scientists Kenneth D. Forbus (Northwestern University), Benjamin Kuipers (University of Michigan), and Henry Lieberman (Massachusetts Institute of Technology) recall their interactions with Minksy and briefly recount the impact he had on their lives and their research. A remembrance of Marvin Minsky was held at the AAAI Spring Symposium at Stanford University on March 22. Video remembrances of Minsky by Danny Bobrow, Benjamin Kuipers, Ray Kurzweil, Richard Waldinger, and others can be on the sentient webpage1 or on youtube.com.


An existing, ecologically-successful genus of collectively intelligent artificial creatures

arXiv.org Artificial Intelligence

People sometimes worry about the Singularity [Vinge, 1993; Kurzweil, 2005], or about the world being taken over by artificially intelligent robots. I believe the risks of these are very small. However, few people recognize that we already share our world with artificial creatures that participate as intelligent agents in our society: corporations. Our planet is inhabited by two distinct kinds of intelligent beings --- individual humans and corporate entities --- whose natures and interests are intimately linked. To co-exist well, we need to find ways to define the rights and responsibilities of both individual humans and corporate entities, and to find ways to ensure that corporate entities behave as responsible members of society.


Autonomously Learning an Action Hierarchy Using a Learned Qualitative State Representation

AAAI Conferences

There has been intense interest in hierarchical reinforcement learning as a way to make Markov decision process planning more tractable, but there has been relatively little work on autonomously learning the hierarchy, especially in continuous domains. In this paper we present a method for learning a hierarchy of actions in a continuous environment. Our approach is to learn a qualitative representation of the continuous environment and then to define actions to reach qualitative states. Our method learns one or more options to perform each action. Each option is learned by first learning a dynamic Bayesian network (DBN). We approach this problem from a developmental robotics perspective. The agent receives no extrinsic reward and has no external direction for what to learn. We evaluate our work using a simulation with realistic physics that consists of a robot playing with blocks at a table.


AAAI 2007 Spring Symposium Series Reports

AI Magazine

The 2007 Spring Symposium Series was held Monday through Wednesday, March 26-28, 2007, at Stanford University, California. The titles of the nine symposia in this symposium series were (1) Control Mechanisms for Spatial Knowledge Processing in Cognitive/Intelligent Systems, (2) Game Theoretic and Decision Theoretic Agents, (3) Intentions in Intelligent Systems, (4) Interaction Challenges for Artificial Assistants, (5) Logical Formalizations of Commonsense Reasoning, (6) Machine Reading, (7) Multidisciplinary Collaboration for Socially Assistive Robotics, (8) Quantum Interaction, and (9) Robots and Robot Venues: Resources for AI Education.


AAAI 2007 Spring Symposium Series Reports

AI Magazine

The 2007 Spring Symposium Series was held Monday through Wednesday, March 26-28, 2007, at Stanford University, California. The titles of the nine symposia in this symposium series were (1) Control Mechanisms for Spatial Knowledge Processing in Cognitive/Intelligent Systems, (2) Game Theoretic and Decision Theoretic Agents, (3) Intentions in Intelligent Systems, (4) Interaction Challenges for Artificial Assistants, (5) Logical Formalizations of Commonsense Reasoning, (6) Machine Reading, (7) Multidisciplinary Collaboration for Socially Assistive Robotics, (8) Quantum Interaction, and (9) Robots and Robot Venues: Resources for AI Education.