Learning Opportunity Costs in Multi-Robot Market Based Planners

Abstract

Abstract-Direct human control of multi-robot systems is limited by the cognitive ability of humans to coordinate numerous interacting components. In remote environments, such as those encountered during planetary or ocean exploration, a further limit is imposed by communication bandwidth and delay.Market based planning can give humans a higher-level interface to multi-robot systems in these scenarios. Operators provide high level tasks and attach a reward to the achievement of each task. The robots then trade these tasks through a market based mechanism. The challenge for the system designer is to create bidding algorithms for the robots that yield high overall system performance.Opportunity cost provides a nice basis for such bidding algorithms since it encapsulates all the costs and benefits we are interested in. Unfortunately, computing it can be difficult. We propose a method of learning opportunity costs in market based planners. We provide analytic results in simplified scenarios and empirical results on our FIRE simulator, which focuses on exploration of Mars by multiple, heterogeneous rovers.Index Terms-Market-based planning, learning, opportunity cost, multi-robot systems.

Imitation learning for task allocation

“…After collecting a large number of example allocations and their associated penalties, they use regression to improve future bidding by creating a model for the task's actual value given the agent's current schedule and other task features. Similarly, Schneider et al learn (online) the opportunity cost for taking on tasks in a market-based multi-robot scenario [14].…”

Market-Based Multirobot Coordination: A Comprehensive Survey and Analysis

“…Most existing marketbased approaches fall into the SR-ST category in the task allocation taxonomy. Several assume instantaneous assignment (IA) [24,39,60,65,68], while others allow for timeextended assignment (TA), introducing an additional layer of planning whereby robots sequence [40,4,11,28,50,51,75] or schedule [26,43,58] a list of tasks and can therefore explicitly reason about the dependencies between multiple tasks and upcoming commitments. More recently, market-based systems have addressed the allocation of multiple-robot tasks (MR-ST) [29,45], including human-robot tasks [33].…”

“…Additionally, solution quality depends on accurate cost and utility measures which may be very challenging to aquire. Although some progress has been made in methods for learning [58] and improving [13] these estimates, further work is required.…”

“…Market-based approaches are well suited to meet this challenge because auctions can simplify the problem of reasoning about team skills. When a task or role is auctioned, each robot's bid encapsulates its ability to complete the task in terms of resource usage, the estimated solution quality afforded by these resources, or even the opportunity cost of forgoing other tasks [58]. Additionally, robots can abstain from bidding on tasks for which they do not have sufficient resources, thereby reducing the computational burden for both bidder and auctioneer [24,31].…”

“…Additionally, robots can abstain from bidding on tasks for which they do not have sufficient resources, thereby reducing the computational burden for both bidder and auctioneer [24,31]. The bids can also encode the solution quality afforded by each member's resources, and even the opportunity cost of forgoing other tasks [58]. The auctioneer can award roles or tasks to team members according to the best bid, without requiring knowledge of individual capabilities.…”