Recent Advances in Robot Learning

Judy A. Franklin, Tom M. Mitchell, and Sebastian Thrun

  • In "Real-World Robotics: Learning To Plan for Robust Execution," Bennett and DeJong introduce an approach called permissive planning, where the permissiveness of a plan is a measure of how closely the plan's preconditions must match the real-world for the plan to succeed. A combination of explanation-based learning to acquire plan schemata and a new approach to the refinement of plans is described, along with an implementation on a hardware robot arm.

  • "Robot Programming by Demonstration (RPD): Supporting the Induction by Human Interaction," by Friedrich et al. combines analytical and inductive learning to generalize the notion of teaching a robot by example, using only a few examples of the proper sequence of motions. On top of this is dialog based learning, a series of questions and answers that occurs while the human is demonstrating. This additional level helps the system determine the intent of the human and narrows the hypothesis space. Implementation involves a physical robot arm.

  • Chen et al. present "Performance Improvement of Robot Continuous-Path Operation through Iterative Learning Using Neural Networks." Performance improvement of continuous path operation is the control engineer's approach to solving the problem of explicitly teaching every move to a robot. This paper has a tutorial nature in that concepts from engineering robotics, such as closed-loop stability and PID control are clearly described.

  • "Learning Controllers for Industrial Robots," by Baroglio et al. is a summarization and comparison of a number of machine learning techniques designed for nonlinear systems. Included are Multilayer Perceptrons, Radial Basis Functions, and Fuzzy Controllers. The comparison takes the form of algorithmic and empirical analysis. This sets the stage for the description of two original integrated learning algorithms.

  • In "Active Learning for Vision-Based Robot Grasping," Salganicoff et al. employ a new integrated learning algorithm, IE-ID3, to give active-learning ability to a robot arm equipped with a vision system. The task is to choose appropriate grasping approaches in order to pick up various objects. Two important objectives are for the algorithm to produce real-valued actions and for learning to occur quickly.

  • "Purposive Behavior Acquisition for a Real Robot by Vision-Based Reinforcement Learning," by Asada et al. describes a mobile robot that learns to shoot a ball into a goal, using Q-learning. Environmental information is given only by the visual image. In addition to Q-learning, the authors employ a learning schedule called Learning from Easy Missions. This paradigm includes an algorithmic decision maker for shifting to more difficult tasks.

  • The final paper, "Learning Concepts from Sensor Data of a Mobile Robot" by Klingspor et al., also uses a mobile robot as a platform to explore the use of machine learning in the link between low-level representations of sensing and action and the high-level representation of planning. The applications addressed involve robots that are not completely autonomous, but interact with human users. The machine learning technique employed is inductive logic programming with some modifications such as bounding the number of rules and predicates as well as splitting the overall task into several learning steps.