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Sensitivity Analysis for Building Evolving & Adaptive Robotic Software Sensitivity Analysis For Building Evolving & Adaptive Robotic Software Prasad Kawthekar Christian Kastner¨ Carnegie Mellon University Abstract researchers to devote more time to the creative process of de- signing new algorithms than on fine-tuning robotic algorithms. There has been a considerable growth in research Software-level design choices are not the only factors that and development of service robots in recent years. affect the quality of performance of a robot. Changes in the For deployment in diverse environment conditions robot’s environment or in its high-level intents may also sig- for a wide range of service tasks, novel features and nificantly affect the robot’s quality of performance. However, algorithms are developed and existing ones undergo negative effects can possibly be mitigated by reconfiguring change. However, developing and evolving the robot the robot’s software at run-time (by reconsidering its design software requires making and revising many design choices) in response to the external changes. Such adaptation decisions that can affect the quality of performance techniques are important to service robots, as they are increas- of the robots and that are non-trivial to reason about ingly deployed in dynamic environments to perform tasks with intuitively because of interactions among them. We diverse performance requirements. propose to use sensitivity analysis to build models All these use cases, from initial decision making to evolu- of the quality of performance to the different design tion and runtime adaptation require making ‘good’ design deci- decisions to ease design and evolution. Moreover, sions at the software level. However, searching for good design we envision these models to be used for run-time choices (or a good software configuration) is not straightfor- adaptation in response to changing goals or environ- ward. The brute-force technique of iterating over all possible ment conditions. Constructing these models is chal- configurations to select the best performing one is infeasible in lenging due to the exponential size of the decision practice, because the search space of possible configurations is space. We build on previous work on performance exponential in the number of design decisions. Multiple design influence models of highly-configurable software decisions can interact with each other to determine the quality systems using a machine-learning-based approach of performance, such that their combined effect is different to construct influence models for robotic software. from the effect of changing them one at a time. This size of the search space motivates a machine-learning 1 Introduction strategy for sensitivity analysis [Saltelli et al., 2000] of robotic software, sampling a few configurations from the search space Designing robotic software requires making many design de- to predict the quality attributes for the remaining configura- cisions. These software-level design decisions (or options) tions. A function learned in the process describes the sen- can impact the quality of performance of the robots. Decision sitivity or influence of different design decisions on quality making in practice is often guided by a mix of intuition, ex- attributes, which can be used to guide optimization, evolu- perience, and some experimentation. However, such informal tion, and runtime adaptation, as well as, for understanding and techniques may not be reliable and can lead to suboptimal debugging the software implementation of robotic algorithms. quality of performance. This can be a serious issue for service We build on previous work on using machine learning robots, as they can often work in close proximity to humans for building influence models of highly-configurable soft- and thus need to operate under stringent requirements on per- ware systems [Siegmund et al., 2015]. We demonstrate fea- formance such as safety and reliability. For instance, poor sibility by applying this approach to the robotics domain decisions may cause service robots to run out of battery power with a preliminary analysis of decisions in the simulator of while delivering tours to visitors. the CoBot robotic platform [Veloso et al., 2015]. Specifi- Therefore, a (formal) technique to guide good software- cally, we focus our preliminary analysis on a subset of de- level design choices that can ensure high quality of perfor- sign choices in the particle-filter based autonomous localiza- mance in robots is desirable. Such a technique can also speed tion component of the CoBot software [Biswas et al., 2011; up evolution of robotic software by helping revisions and Biswas and Veloso, 2013]. We demonstrate the possibility of decision making involved in the integration of new features building evolving and adaptive robotic software by discussing and algorithms with the existing platforms, thereby allowing tradeoffs in the quality requirements for the localization mod- ule and the possibility of countering changes in the external features or algorithms introduces tradeoffs in the perfor- environment by reconfiguring the module; the necessity for mance requirements of the robot, which can cause the machine learning due to the exponential configuration space previously selected design choices to not be suitable for of the module; and a simple linear-regression based influence the new performance requirements. For example, Fig- model for the localization module. ure 1a and Figure 1b illustrate how the configuration space defined by the number of particles and number of 2 Motivation refinements in the localization module exposes a tradeoff between localization error and CPU utilization: Increas- A robot’s performance requirements for completing a task are ing the number of particles (resp. refinements) reduces defined by its high-level goals and intents. The robot’s fidelity the localization error (up to a threshold), but it comes at to its high-level goals can be measured using appropriately de- the expense of higher CPU utilization. To handle tradeoffs fined quality attributes. For instance, the high-level goals of an between different performance requirements, different autonomous robot while performing a navigation task would influence models can be learned for each corresponding be to complete the task as fast as possible while using minimal quality attributes and then used together to guide the processing power (corresponding to longer battery life). The (re)configuration of the robotic software. robot’s quality of performing the navigation task can thus be measured in terms of the total time to complete the task, its to- • Runtime Adapting to Change. Influence models can tal power consumption, and the accuracy of its localization dur- also be used to build robotic software that can purpose- ing the task. Optimizing these quality measures would thus en- fully adapt to changes at runtime. Both environment able the robot to more effectively achieve its high-level goals. conditions and performance requirements can vary at run- The software of a robot implements algorithms that enable time depending on the task assigned to the robot. For the robot to achieve its goals, such as state estimation and plan- adaptation, the robot can use a learned influence model ning. The quality of the algorithms and their implementation to tweak its software configuration options and move is thus responsible for determining the quality of performance to a more optimal point in the configuration space. For of the robot. Designing this software involves many choices, instance, our experiments in Figure 2 indicate that an which can manifest as constants or variables in the source increase in the number of refinements can lead to lower code and can range in granularity, from selecting between localization error in the face of higher odometry noise and different types of algorithms for a task to selecting values miscalibration, at the expense of higher CPU utilization. for some algorithm’s parameters. Consider the particle filter At a more coarse granularity, the robot could adapt by based localization algorithm of CoBots [Biswas et al., 2011; switching between different types of robotic localization Biswas and Veloso, 2013]: Implementing this algorithm re- algorithms altogether, each designed for slightly different quires making a choice for the number of particle estimates optimization problems and potentially better suited for and the number of gradient descent refinement steps applied to some environments than others. Rather than implement- each of these particles on sensor updates. Different values for ing a single localization algorithm in the robot’s software, these options can impact the quality of performance of a robot. multiple algorithms can be implemented and the choice For example, in Figure 1a and Figure 1b, we show that differ- between them be left open depending on the environment ent decisions for these two parameters can have a significant conditions. Such choice can be incorporated in influence effect on the average localization error (the euclidean distance models as a single variable. Quality measures are likely between the true location and the estimated location) and aver- to be more sensitive to coarser configuration options than age CPU utilization of the robot in a sample navigation task. fine-grained ones, thereby implementing coarser config- An influence model that describes the effects (or sensitivity) urations can open up avenues for greater adaptability of different decisions on the quality of performance can be and larger gains in optimization.
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