Learning to Plan by Learning Rules

Many environments involve following rules and tasks; for example, a chef cooking a dish follows a recipe, and a person driving follows rules of the road. People are naturally fluent with rules: we can learn rules efficiently; we can follow rules; we can interpret rules and explain them to others; and we can rapidly adjust to modified rules such as a new recipe without needing to relearn everything from scratch. By contrast, deep reinforcement learning (DRL) algorithms are ill-suited to learning policies in rule-based environments, as satisfying rules often involves executing lengthy tasks with sparse rewards. Furthermore, learned DRL policies are difficult if not impossible to interpret and are not composable. The aim of this thesis is to develop a reinforcement learning framework for rule-based environments that can efficiently learn policies that are interpretable, satisfying, and composable. We achieve interpretability by representing rules as automata or Linear Temporal Logic (LTL) formulas in a hierarchical Markov Decision Process (MDP). We achieve satisfaction by planning over the hierarchical MDP using a modified version of value iteration. We achieve composability by building off of a hierarchical reinforcement learning (HRL) framework called the options framework, in which low-level options can be composed arbitrarily. And lastly, we achieve data-efficient learning by integrating our HRL framework into a Bayesian model that can infer a distribution over LTL formulas given a low-level environment and a set of expert trajectories. We demonstrate the effectiveness of our approach via a number of rule-learning and planning experiments in both simulated and real-world environments.Ph.D

Design and control of miniature air-and-ground vehicles

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2017.Cataloged from PDF version of thesis.Includes bibliographical references (pages 89-92).The ability to both fly and drive is a superpower that few robots have. This thesis describes the design and control of two miniature air-and-ground vehicles, the "Flying Monkey" and the "Flying Car." The Flying Monkey was developed to demonstrate the viability and utility of miniature air-and-ground vehicles. The final design weighs 30g yet is capable of crawling, grasping, and flying. It features a novel crawling and grasping mechanism that consists of 66 linkages yet weighs only 5.1 grams. Although the crawler is capable of only forward and backward motion, we designed a controller that uses the yaw torque of the propellers to give the Flying Monkey two degrees of freedom on the ground. In experiments we demonstrated that the Flying Monkey is able to grasp small objects, fly over obstacles, and crawl through narrow pipes. The Flying Car was designed as a swarm vehicle to test multi-robot path planning. We therefore made the Flying Car as simple and robust as possible, built a small swarm of them, and tested them in a miniature town. We present two of the first algorithms for multi-robot path planning for air-and-ground vehicles, one based on priority planning and the other based on multi-commodity network flow. Thus, by designing and testing robots, controllers, and algorithms for miniature air-and-ground vehicles, this thesis hopes to serve as a starting point for future research in this promising area of study.by Minoru Brandon Araki.S.M

A Modular Folded Laminate Robot Capable of Multi Modal Locomotion

This paper describes fundamental principles for two-dimensional pattern design of folded robots, specifically mobile robots consisting of closed-loop kinematic linkage mechanisms. Three fundamental methods for designing closed-chain folded four-bar linkages – the basic building block of these devices – are introduced. Modular connection strategies are also introduced as a method to overcome the challenges of designing assemblies of linkages from a two-dimensional sheet. The result is a design process that explores the tradeoffs between the complexity of linkage fabrication and also allows the designer combine multiple functions or modes of locomotion. A redesigned modular robot capable of multi-modal locomotion and grasping is presented to embody these design principles.National Science Foundation (Grants EFRI-1240383 and CCF- 1138967