1. Thrust IIB: Dynamic Task Allocation in Remote Multi-robot HRI Jon How (lead) Nick Roy MURI 8 Kickoff Meeting 2007

2. MIT-Vanderbilt-Stanford UW-UMASS Amherst MURI 8 Kickoff Meeting 2007 Task Objective  Objective: develop a robust interactive planner for assigning tasks and responsibilties to the multiple agents  Complex problem for heterogeneous teams with numerous time- varying capabilities and coupled tasks:  Must find dynamically feasible trajectories  Task assignment with tightly coupled tasks is combinatorial problem  Further complicated by uncertainty in the situational awareness and asset requirements for future missions  Numerous planning algorithms already exist -- research issues include:  Developing computationally tractable algorithms for distributed real-time team planning  Ensuring robustness to uncertainty / modeling errors / different environmental abstractions within the team  Human / planner / robot integration and interfaces  Mixed initiative control with humans “in” and “on” the loop

3. MIT-Vanderbilt-Stanford UW-UMASS Amherst MURI 8 Kickoff Meeting 2007 Planning Algorithms  Algorithms developed for each planning component  Receding horizon with repeated optimizations on-line  Numerous programming frameworks - MDP, MIP, NLP  Robustness to uncertainty and filtering enables both reactive and proactive response without excessive changes or “churning”  Consensus algorithms to enable efficient sharing of information Trajectory Design Actuation Mission Planning Task Assignment Vehicle/Obstacle/Target States Environment Estimator Vehicle Controllers Waypoint plans Mission Objectives

4. MIT-Vanderbilt-Stanford UW-UMASS Amherst MURI 8 Kickoff Meeting 2007 Dynamic Task Allocation for Distributed H-R Teams  Issues:  Heterogeneous team (UAVs, UGVs) with humans as mobile agents (not just operators)  Local constraints such as physical capabilities, computational or communication limitations, cognitive load, and attention span  Various levels of team connectivity  From nearby HR teammates to looser, lower-bandwidth collaboration between a robot team and remote commander  Must adapt to changes in the constraints as teams form/move and complete tasks  Need to integrate joint action policies encoding human preference models, capabilities and constraints with the task allocation process  Each “task primitive” is a complete joint-action policy computed using interaction planning algorithm (e.g., POMDP)  Need to extract high-level features of the cognitive model and the natural language communication for the task allocation, compared to within-team action selection  Determine key parameters that determine how to adapt these models in real-time, e.g. stress level, amount of information available to human  Metrics to determine these parameters

5. MIT-Vanderbilt-Stanford UW-UMASS Amherst MURI 8 Kickoff Meeting 2007 Technical Innovations  Quantitative models for combined human-robot teams must be developed, not only to analyze team performance but also to design tasking protocols  H/R have very different capabilities and will perform different tasks in the planning loop.  Must allocate these roles using the current human state and the environment task  Task models must accurately capture the team dynamics in terms of:  Task capability (e.g., can this human-robot team perform this triage?),  Changing communication constraints,  Individual human preference models  Tasking algorithms must integrate seamlessly with the humans  How to effectively modify the different planners to enable suitable operator interaction  Task planning is data and computation intensive, typically using algorithms that are well beyond the scope of human capabilities, especially in stressful environments.  Consensus algorithms require computationally intensive iterative filtering process  Human has much to contribute, but how participate?  Must develop better methods to convey the uncertainty of the current situational awareness (SA) to the human; and enable the human to provide feedback on the SA and succinctly codify their doubts about any new data  Depends on current human state and the amount / accuracy of their information

6. MIT-Vanderbilt-Stanford UW-UMASS Amherst MURI 8 Kickoff Meeting 2007 Technical Innovations  Tasking representations must integrate with human mental models, which are typically more abstract than in planner  Task allocation and team-based action selection must share common representation so that tasks can be converted into local plans  Resulting plans must be conveyed to the human in a way that can be readily understood and queried for more information  Provide teams with enough information (but not too much)  Example: if more urgent situation develops in another area, planner might wantto reassign the robots to new tasks, but the human should have enough information to know why the robots were reallocated  Dialogue interaction system must be able to identify which components of the global plan are relevant to the human teammate, and be able to express that information in a shared abstraction  Involves a trade-off between actions that maximize utility and actions that maximize information  Joint-action selection policy encoded using a POMDP will provide one way to do this  Motivates development of models of human preference, so that planner can provide dynamic re-allocations when humans are prepared  Determine “rules of interaction” for humans / planner interface  E.g. if human and planner disagree on relative importance of the tasks, how should the planner proceed?  Functions of many parameters (stress level, information available)

7. MIT-Vanderbilt-Stanford UW-UMASS Amherst MURI 8 Kickoff Meeting 2007 Milestones  Year 1: Demonstrate HRI between robots (UAVs and UGVs) and remote human operators in medical triage scenario  Aerial robots flying around with camera. Mapping and finding targets. Some moving some stationary. Some human some not. Label ares of interest that need more exploration by ground robots.  Based on that map, mobile robots go out into hot zone and investigate regions highlighted by aerial robots. Tools for remote medics to get closer view, talk to victims through robot. Robot can give a medical triage tag to person. Administer some medical treatment using some medical sensing of MDS in hands, etc.

8. MIT-Vanderbilt-Stanford UW-UMASS Amherst MURI 8 Kickoff Meeting 2007 Milestones  Year 2: Human-robot peer-to-peer in a hot zone.  Demonstrate integration of task allocation, cognitive models, state estimation and joint action selection  1 human and 2 MDS and 2 Arial robots in the hot zone  Task allocation after entering hot zone:  Search and Surveillance for a robot-robot team  Human-robot trained sample collection  Dynamic reallocation when new tasks found  Year 3: HRI with peer-to-peer and remote robots  Develop and evalute fully integrated architecture