Guest Editorial: Special issue on constrained decision-making in robotics

Noname manuscript No. (will be inserted by the editor) Guest Editorial: Special Issue on Constrained Decision-Making in Robotics Marco Pavone · Stefano Carpin the date of receipt and acceptance should be inserted later As the complexity of tasks envisioned for robotic systems increases, their decision making module needs to be capable of concurrently evaluating and trading off multiple, possibly contradicting and stochastic ob- jectives. For example, a rescue robot might be required to plan trajectories so as to maximize the probability of success to reach a critical location and, at the same time, minimize the duration of the traversal. Other more general examples include trading off information gath- ering versus energy expenditures, computation time ver- sus optimality of a decision, or learning versus safety. A natural framework to address this class of problems is constrained decision-making, whereby a decision maker seeks to optimize a given cost function (often stochas- tic) while keeping other costs (usually involving risk assessments) below given bounds. In the last decade, the operations research community has made significant strides on the topics of constrained decision-making (notoriously more challenging than the unconstrained counterpart) and risk assessment in dynamic scenarios. The result is a comprehensive theory and a set of algo- rithmic tools for this class of decision-making problems. Yet, despite their relevance, these results have seen lim- ited application within the robotics domain. Accordingly, in July 2014 we organized the work- shop “Constrained decision-making in robotics: models, algorithms, and applications” during the Robotics Sci- ence and Systems (RSS) conference in Berkeley, CA. Marco Pavone Department of Aeronautics and Astronautics Stanford University E-mail: pavone@stanford.edu Stefano Carpin School of Engineering University of California, Merced E-mail: scarpin@ucmerced.edu The workshop had three objectives: (1) to convene to- gether researchers working in the areas of decision-making, risk theory, and robotics, (2) to inform robotic researchers about the state of the art in constrained decision-making and modern risk theory, and (3) to formulate a research agenda on the topics of risk modeling and constrained decision-making for robotic applications. After the workshop, participants were invited to sub- mit an extended version of their work. The call for con- tributions was also extended to the broader research community and widely circulated. Eventually, six pa- pers were selected for publication and are included in this special issue. Collectively, these papers covers sev- eral robotic application domains for constrained decision- making, ranging from sensors networks to autonomous vehicles and space robotics. The first three papers deal with a deterministic prob- lem setup. Specifically, the paper “Cognitive Robots Learning Failure Contexts through Real-World Exper- imentation” by Sertac Kapinar and Sanem Sariel stud- ies how to endow robots with the ability to learn about their limitations of performance when it comes to task execution, and to capitalize on such knowledge to for- mulate risk-averse decisions subject to performance con- straints. The authors tackle the problem by using an In- ductive Logic Programming (ILP) framework, whereby hypotheses are formulated that relate execution con- texts to action outcomes. Hypotheses are continuously updated based on online observations and are used to model risks, constraints, and limitations on task exe- cution. On top of this knowledge base, a planner for- mulates decision sequences fulfilling the constraints de- rived from the learned hypotheses. The theoretical frame- work is experimentally demonstrated and validated both on a mobile robot and on a 7-DOF robotic arm perform-