Safe and Efficient Robot Action Choice Using Human Intent Prediction in Physically-Shared Space Environments.

Emerging robotic systems are capable of autonomously planning and executing well-defined tasks, particularly when the environment can be accurately modeled. Robots supporting human space exploration must be able to safely interact with human astronaut companions during intravehicular and extravehicular activities. Given a shared workspace, efficiency can be gained by leveraging robotic awareness of its human companion. This dissertation presents a modular architecture that allows a human and robotic manipulator to efficiently complete independent sets of tasks in a shared physical workspace without the robot requiring oversight or situational awareness from its human companion. We propose that a robot requires four capabilities to act safely and optimally with awareness of its companion: sense the environment and the human within it; translate sensor data into a form useful for decision-making; use this data to predict the human’s future intent; and then use this information to inform its action-choice based also on the robot’s goals and safety constraints. We first present a series of human subject experiments demonstrating that human intent can help a robot predict and avoid conflict, and that sharing the workspace need not degrade human performance so long as the manipulator does not distract or introduce conflict. We describe an architecture that relies on Markov Decision Processes (MDPs) to support robot decision-making. A key contribution of our architecture is its decomposition of the decision problem into two parts: human intent prediction (HIP) and robot action choice (RAC). This decomposition is made possible by an assumption that the robot’s actions will not influence human intent. Presuming an observer that can feedback human actions in real-time, we leverage the well-known space environment and task scripts astronauts rehearse in advance to devise models for human intent prediction and robot action choice. We describe a series of case studies for HIP and RAC using a minimal set of state attributes, including an abbreviated action-history. MDP policies are evaluated in terms of model fitness and safety/efficiency performance tradeoffs. Simulation results indicate that incorporation of both observed and predicted human actions improves robot action choice. Future work could extend to more general human-robot interaction.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107160/1/cmcghan_1.pd

Human Intent Prediction Using Markov Decision Processes

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140661/1/1.i010090.pd

A risk-aware architecture for resilient spacecraft operations

In this paper we discuss a resilient, risk-aware software architecture for onboard, real-time autonomous operations that is intended to robustly handle uncertainty in space-craft behavior within hazardous and unconstrained environments, without unnecessarily increasing complexity. This architecture, the Resilient Spacecraft Executive (RSE), serves three main functions: (1) adapting to component failures to allow graceful degradation, (2) accommodating environments, science observations, and spacecraft capabilities that are not fully known in advance, and (3) making risk-aware decisions without waiting for slow ground-based reactions. This RSE is made up of four main parts: deliberative, habitual, and reflexive layers, and a state estimator that interfaces with all three. We use a risk-aware goal-directed executive within the deliberative layer to perform risk-informed planning, to satisfy the mission goals (specified by mission control) within the specified priorities and constraints. Other state-of-the-art algorithms to be integrated into the RSE include correct-by-construction control synthesis and model-based estimation and diagnosis. We demonstrate the feasibility of the architecture in a simple implementation of the RSE for a simulated Mars rover scenario

Human Productivity in a Workspace Shared with a Safe Robotic Manipulator

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140515/1/1.54993.pd

Risk-aware Planning in Hybrid Domains: An Application to Autonomous Planetary Rovers

Expanding robotic space exploration beyond the immediate vicinity of Earth's orbit can only be achieved by increasingly autonomous agents, given the sometimes insurmountable challenges of teleoperation over great distances. Among the numerous requirements that a fully autonomous robotic space explorer must meet, here we focus on three key mission-enabling technologies: the ability to act under uncertainty and adapt to its environment; the ability to optimize performance while offering hard bounds on the risk of mission failure; and the ability to handle complex hybrid (discrete and continuous) mission planning problems in a provably correct and scalable fashion. In this paper, we show how CLARK, a planner capable of generating risk-bounded, dynamic temporal plans for autonomous agents operating under uncertainty, is able to efficiently generate temporal plans for a challenging planetary rover scenario featuring temporal uncertainty that could not be addressed by previous methods

Risk-aware Planning in Hybrid Domains: An Application to Autonomous Planetary Rovers

Expanding robotic space exploration beyond the immediate vicinity of Earth's orbit can only be achieved by increasingly autonomous agents, given the sometimes insurmountable challenges of teleoperation over great distances. Among the numerous requirements that a fully autonomous robotic space explorer must meet, here we focus on three key mission-enabling technologies: the ability to act under uncertainty and adapt to its environment; the ability to optimize performance while offering hard bounds on the risk of mission failure; and the ability to handle complex hybrid (discrete and continuous) mission planning problems in a provably correct and scalable fashion. In this paper, we show how CLARK, a planner capable of generating risk-bounded, dynamic temporal plans for autonomous agents operating under uncertainty, is able to efficiently generate temporal plans for a challenging planetary rover scenario featuring temporal uncertainty that could not be addressed by previous methods

Resilient Spacecraft Executive: An Architecture for Risk-Aware Operations in Uncertain Environments

In this paper we discuss the latest results from the Resilient Space Systems project, a joint effort between Caltech, MIT, NASA Jet Propulsion Laboratory (JPL), and the Woods Hole Oceanographic Institution (WHOI). The goal of the project is to define a resilient, risk-aware software architecture for onboard, real-time autonomous operations that can robustly handle uncertainty in spacecraft behavior within hazardous and unconstrained environments, without unnecessarily increasing complexity. The architecture, called the Resilient Spacecraft Executive (RSE), has been designed to support three functions: (1) adapting to component failures to allow graceful degradation, (2) accommodating environments, science observations, and spacecraft capabilities that are not fully known in advance, and (3) making risk-aware decisions without waiting for slow ground-based reactions. In implementation, the bulk of the RSE effort has focused on the parts of the architecture used for goal-directed execution and control, including the deliberative, habitual, and reflexive modules. We specify the capabilities and constraints needed for each module, and discuss how we have extended the current state-of-the-art algorithms so that they can supply the required functionality, such as risk-aware planning in the deliberative module that conforms to mission operator-supplied priorities and constraints. Furthermore, the RSE architecture is modular to enable extension and reconfiguration, as long as the embedded algorithmic components exhibit the required risk-aware behavior in the deliberative module and risk-bounded behavior in the habitual module. To that end, we discuss some feasible, useful RSE configurations and deployments for a Mars rover case and an autonomous underwater vehicle case. We also discuss additional capabilities that the architecture requires to support needed resiliency, such as onboard analysis and learning