Caroline Ponzoni Carvalho Chanel

MOMDP-Based Target Search Mission Taking into Account the Human Operator's Cognitive State

This study discusses the application of sequential decision making under uncertainty and mixed observability in a mixed-initiative robotic target search application. In such a robotic mission, two agents, a ground robot and a human operator, must collaborate to reach a common goal using, each in turn, their recognized skills. The originality of the work relies in considering that the human operator is not a providential agent when the robot fails. Using the data from previous experiments, a Mixed Observability Markov Decision Process (MOMDP) model was designed, which allows to consider aleatory failure events and the partial observable human operators state while planning for a long-term horizon. Results show that the collaborative system was in general able to successfully complete or terminate the mission, even when many simultaneous sensors, devices and operator failures happened. So, the mixed-initiative framework highlighted in this study shows the relevancy of taking into account the cognitive state of the operator, which permits to compute a policy for the sequential decision problem which prevents to re-planning when unexpected (but known) events occurs.

Considering human's non-deterministic behavior and his availability state when designing a collaborative human-robots system

The objective of this study is to design a human-robots system that takes into account the non-deterministic nature of the human operators behavior. Such a system is implemented in a proof of concept scenario relying on a (MO)MDP decision framework that takes advantage of an eye-tracker device to estimate the cognitive availability of the human operator, and, some human operators inputs to deduce where he is focusing his attention. An experiment was conducted with ten participants interacting with a team of autonomous vehicles in a Search & Rescue scenario. Our results demonstrate the advantages of considering the cognitive availability of a human operator in such a complex context and also the interest of using such a decisional framework that can formally integrate the non-deterministic outcomes which model the human behavior.

Pre-stimulus antero-posterior EEG connectivity predicts performance in a UAV monitoring task

Long monitoring tasks without regular actions, are becoming increasingly common from aircraft pilots to train conductors as these systems grow more automated. These task contexts are challenging for the human operator because they require inputs at irregular and highly interspaced moments even though these actions are often critical. It has been shown that such conditions lead to divided and distracted attentional states which in turn reduce the processing of external stimuli (e.g. alarms) and may lead to miss critical events. In this study we explored to which extent it is possible to predict an operators behavioural performance in a Unmanned Aerial Vehicle (UAV) monitoring task using electroencephalographic (EEG) activity. More specifically we investigated the relevance of large-scale EEG connectivity for performance prediction by correlating relative coherence with reaction times (RT). We show that long-range EEG relative coherence, i.e. between occipital and frontal electrodes, is significantly correlated with RT and that different frequency bands exhibit opposite effects. More specifically we observed that coherence between occipital and frontal electrodes was: negatively correlated with RT at 6Hz (θ band), more coherence leading to better performance, and positively correlated with RT at 8Hz (lower α band), more coherence leading to worse performance. Our results suggest that EEG connectivity measures could be useful in predicting an operators attentional state and her/his performances in ecological settings. Hence these features could potentially be used in a neuro-adaptive interface to improve operator-system interaction and safety in critical systems.

Properly Acting under Partial Observability with Action Feasibility Constraints

We introduce Action-Constrained Partially Observable Markov Decision Process (AC-POMDP), which arose from studying critical robotic applications with damaging actions. AC-POMDPs restrict the optimized policy to only apply feasible actions: each action is feasible in a subset of the state space, and the agent can observe the set of applicable actions in the current hidden state, in addition to standard observations. We present optimality equations for AC-POMDPs, which imply to operate on α-vectors defined over many different belief subspaces. We propose an algorithm named PreCondition Value Iteration (PCVI), which fully exploits this specific property of AC-POMDPs about α-vectors. We also designed a relaxed version of PCVI whose complexity is exponentially smaller than PCVI. Experimental results on POMDP robotic benchmarks with action feasibility constraints exhibit the benefits of explicitly exploiting the semantic richness of action-feasibility observations in AC-POMDPs over equivalent but unstructured POMDPs.

AMPLE: an anytime planning and execution framework for dynamic and uncertain problems in robotics

Acting in robotics is driven by reactive and deliberative reasonings which take place in the competition between execution and planning processes. Properly balancing reactivity and deliberation is still an open question for harmonious execution of deliberative plans in complex robotic applications. We propose a flexible algorithmic framework to allow continuous real-time planning of complex tasks in parallel of their executions. Our framework, named AMPLE, is oriented towards robotic modular architectures in the sense that it turns planning algorithms into services that must be generic, reactive, and valuable. Services are optimized actions that are delivered at precise time points following requests from other modules that include states and dates at which actions are needed. To this end, our framework is divided in two concurrent processes: a planning thread which receives planning requests and delegates action selection to embedded planning softwares in compliance with the queue of internal requests, and an execution thread which orchestrates these planning requests as well as action execution and state monitoring. We show how the behavior of the execution thread can be parametrized to achieve various strategies which can differ, for instance, depending on the distribution of internal planning requests over possible future execution states in anticipation of the uncertain evolution of the system, or over different underlying planners to take several levels into account. We demonstrate the flexibility and the relevance of our framework on various robotic benchmarks and real experiments that involve complex planning problems of different natures which could not be properly tackled by existing dedicated planning approaches which rely on the standard plan-then-execute loop.

Towards human-robot interaction: A framing effect experiment

Decision making is a critical issue for humans operating unmanned vehicles. However, it is well admitted that many cognitive biases affect human judgments, leading to suboptimal or irrational decisions. The framing effect is a typical cognitive bias causing people to react differently depending on the context, the probability of the outcomes and how the problem is presented (loss vs. gain). There is a need to better understand the effects of these biases in operational contexts to optimize human-robot interactions. We therefore conducted an experiment involving a framing paradigm in a search and rescue mission (earthquake) and in a Mars rock sampling mission. We manipulated the framing (positive vs. negative) and the probability of the outcomes. Our findings revealed that the way the problem was presented (positively or negatively framed) and the emotional commitment (saving lives vs. collecting the good rock) statistically affected the choices made by the human operators.

Multi-robot Cooperative Systems for Exploration: Advances in Dealing with Constrained Communication Environments

In the present document, the authors introduce the Cooperative Exploration problem as well as the most relevant approaches in order to show the most common drawbacks and opportunities to improve the state of art solutions. Subsequently, a preliminary version of a multi-robot exploration proposal is described. The first results obtained in simulated scenarios support the underlying ideas are feasible and promising. They show that is possible to cope with real communication constraints (always present in practice), being more fault tolerant and still having good performance regarding the total exploration time. Next steps to fully implement a more reliable and robust system are discussed.

Navigation and Guidance Strategy Planning for UAV Urban Operation

This paper proposes a concept of navigation and guidance strategy planner for urban operation of a VTOL-type UAV. One of major challenges of UAV autonomous navigation in an urban environment is to deal with the risk of GPS signal occlusion. In order to address this issue, various approaches have been proposed for GPS-independent UAV navigation and guidance such as visual odometry and visual servoing control. In this context, this work supposes that different navigation and guidance modes using different set of sensors are available onboard an UAV. An idea of the proposed planner is to anticipate the navigation and guidance performance degradation (or amelioration) due to unavailability (or availability) of certain mode in the path planning task. The planning problem is formulated as a 5D (3D position + selection of navigation and guidance modes) graph search problem, where the localization and path execution uncertainties are propagated according to a model of corresponding modes for each node transition. Node transition is denied if the path execution uncertainty ellipsoid intersects with any obstacle. A minimizing cost function is defined by a volume of the path execution uncertainty corridor, as it implies minimizing path distance and execution accuracy at the same time. A deterministic graph search algorithm is applied to find a flight path with specified navigation and guidance mode transitions which minimizes the defined cost function. Simulations are performed by using path planning configurations given in an existing UAV obstacle field navigation benchmark, and the results are presented to prove the proposed navigation and guidance strategy planning concept.