Fluid coordination of human-robot teams

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 235-239).I envision a future where collaboration between humans and robots will be indispensable to our work in numerous domains, ranging from surgery to space exploration. The success of these systems will depend in part on the ability of robots to integrate within existing human teams. The goal of this thesis is to develop robot partners that we can work with easily and naturally, inspired by the way we work with other people. My hypothesis is that human-robot team performance improves when a robot teammate emulates the effective coordination behaviors observed in human teams. I design and evaluate Chaski, a robot plan execution system that uses insights from human-human teaming to make human-robot teaming more natural and fluid. Chaski is a task-level executive that enables a robot to robustly anticipate and adapt to other team members. Chaski also emulates a human's response to implicit communications, including verbal and gestural cues, and explicit commands. Development of such an executive is challenging because the robot must be able to make decisions very quickly in response to a human's actions. In the past, the ability of robots to demonstrate these capabilities has been limited by the time-consuming computations required to anticipate a large set of possible futures. These computations result in execution delays that endanger the robot's ability to fulfill its role on the team. I significantly improve the ability of a robot to adapt on-the-fly by generalizing the state-of-the-art in dynamic plan execution to support just-in-time task assignment and scheduling. My methods provide a novel way to represent the robot's plan compactly. This compact representation enables the plan to be incrementally updated very quickly. I empirically demonstrate that, compared to prior work in this area, my methods increase the speed of online computation by one order of magnitude on average. I also show that 89% of moderately-sized benchmark plans are updated within human reaction time using Chaski, compared to 24% for prior art. I evaluate Chaski in human subject experiments in which a person works with a mobile and dexterous robot to collaboratively assemble structures using building blocks. I measure team performances outcomes for robots controlled by Chaski compared to robots that are verbally commanded, step-by-step by the human teammate. I show that Chaski reduces the human's idle time by 85%, a statistically significant difference. This result supports the hypothesis that human-robot team performance is improved when a robot emulates the effective coordination behaviors observed in human teams.by Julie A. Shah.Ph.D

Risk-minimizing program execution in robotic domains

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 153-161).In this thesis, we argue that autonomous robots operating in hostile and uncertain environments can improve robustness by computing and reasoning explicitly about risk. Autonomous robots with a keen sensitivity to risk can be trusted with critical missions, such as exploring deep space and assisting on the battlefield. We introduce a novel, risk-minimizing approach to program execution that utilizes program flexibility and estimation of risk in order to make runtime decisions that minimize the probability of program failure. Our risk-minimizing executive, called Murphy, utilizes two forms of program flexibility, 1) flexible scheduling of activity timing, and 2) redundant choice between subprocedures, in order to minimize two forms of program risk, 1) exceptions arising from activity failures, and 2) exceptions arising from timing constraint violations in a program. Murphy takes two inputs, a program written in a nondeterministic variant of the Reactive Model-based Programming Language (RMPL) and a set of stochastic activity failure models, one for each activity in a program, and computes two outputs, a risk-minimizing decision policy and value function. The decision policy informs Murphy which decisions to make at runtime in order to minimize risk, while the value function quantifies risk. In order to execute with low latency, Murphy computes the decision policy and value function offline, as a compilation step prior to program execution. In this thesis, we develop three approaches to RMPL program execution. First, we develop an approach that is guaranteed to minimize risk. For this approach, we reason probabilistically about risk by framing program execution as a Markov Decision Process (MDP). Next, we develop an approach that avoids risk altogether. For this approach, we frame program execution as a novel form of constraint-based temporal reasoning. Finally, we develop an execution approach that trades optimality in risk avoidance for tractability. For this approach, we leverage prior work in hierarchical decomposition of MDPs in order to mitigate complexity. We benchmark the tractability of each approach on a set of representative RMPL programs, and we demonstrate the applicability of the approach on a humanoid robot simulator.by Robert T. Effinger, IV.Sc.D

A proposal for a global task planning architecture using the RoboEarth cloud based framework

As robotic systems become more and more capable of assisting in human domains, methods are sought to compose robot executable plans from abstract human instructions. To cope with the semantically rich and highly expressive nature of human instructions, Hierarchical Task Network planning is often being employed along with domain knowledge to solve planning problems in a pragmatic way. Commonly, the domain knowledge is specific to the planning problem at hand, impeding re-use. Therefore this paper conceptualizes a global planning architecture, based on the worldwide accessible RoboEarth cloud framework. This architecture allows environmental state inference and plan monitoring on a global level. To enable plan re-use for future requests, the RoboEarth action language has been adapted to allow semantic matching of robot capabilities with previously composed plans

Temporal and Hierarchical Models for Planning and Acting in Robotics

The field of AI planning has seen rapid progress over the last decade and planners are now able to find plan with hundreds of actions in a matter of seconds. Despite those important progresses, robotic systems still tend to have a reactive architecture with very little deliberation on the course of the plan they might follow. In this thesis, we argue that a successful integration with a robotic system requires the planner to have capacities for both temporal and hierarchical reasoning. The former is indeed a universal resource central in many robot activities while the latter is a critical component for the integration of reasoning capabilities at different abstraction levels, typically starting with a high level view of an activity that is iteratively refined down to motion primitives. As a first step to carry out this vision, we present a model for temporal planning unifying the generative and hierarchical approaches. At the center of the model are temporal action templates, similar to those of PDDL complemented with a specification of the initial state as well as the expected evolution of the environment over time. In addition, our model allows for the specification of hierarchical knowledge possibly with a partial coverage. Consequently, our model generalizes the existing generative and HTN approaches together with an explicit time representation. In the second chapter, we introduce a planning procedure suitable for our planning model. In order to support hierarchical features, we extend the existing Partial-Order Causal Link approach used in many constraintbased planners, with the notions of task and decomposition. We implement it in FAPE (Flexible Acting and Planning Environment) together with automated problem analysis techniques used for search guidance. We show FAPE to have performance similar to state of the art temporal planners when used in a generative setting. The addition of hierarchical information leads to further performance gain and allows us to outperform traditional planners. In the third chapter, we study the usual methods used to reason on temporal uncertainty while planning. We relax the usual assumption of total observability and instead provide techniques to reason on the observations needed to maintain a plan dispatchable. We show how such needed observations can be detected at planning time and incrementally dealt with by considering the appropriate sensing actions. In a final chapter, we discuss the place of the proposed planning system as a central component for the control of a robotic actor. We demonstrate how the explicit time representation facilitates plan monitoring and action dispatching when dealing with contingent events that require observation. We take advantage of the constraint-based and hierarchical representation to facilitate both plan-repair procedures as well opportunistic plan refinement at acting time

Coordination for Scalable Multiple Robot Planning Under Temporal Uncertainty

This dissertation incorporates coalition formation and probabilistic planning towards a domain-independent automated planning solution scalable to multiple heterogeneous robots in complex domains. The first research direction investigates the effectiveness of Task Fusion and introduces heuristics that improve task allocation and result in better quality plans, while requiring lower computational cost than the baseline approaches. The heuristics incorporate relaxed plans to estimate coupling and determine which tasks to fuse. As a result, larger temporal continuous planning problems involving multiple robots can be solved. The second research direction introduces new coordination methods to merge plans and resolve conflicts while extending the framework to domains with stochastic action duration. Merging distributedly generated plans becomes computationally costly when task plans are tightly coupled, and conflicts arise due to dependencies between plan actions. Existing methods either scale poorly as the number of agents and tasks increases, or do not minimize makespan, the overall time necessary to execute all tasks. A new family of plan coordination and conflict resolution algorithms is introduced to merge independently generated plans, minimize the resulting makespan, and scale to a large number of tasks and agents in complex problems. A thorough algorithmic analysis and empirical evaluation demonstrates how the new conflict identification and resolution models can impact the resulting plan quality and computational cost across three heterogeneous multiagent domains and outperform the baseline algorithms

Robust execution of bipedal walking tasks from biomechanical principles

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 348-352).Effective use of robots in unstructured environments requires that they have sufficient autonomy and agility to execute task-level commands successfully. A challenging example of such a robot is a bipedal walking machine. Such a robot should be able to walk to a particular location within a particular time, while observing foot placement constraints, and avoiding a fall, if this is physically possible. Although stable walking machines have been built, the problem of task-level control, where the tasks have stringent state-space and temporal requirements, and where significant disturbances may occur, has not been studied extensively. This thesis addresses this problem through three objectives. The first is to devise a plan specification where task requirements are expressed in a qualitative form that provides for execution flexibility. The second is to develop a task-level executive that accepts such a plan, and outputs a sequence of control actions that result in successful plan execution. The third is to provide this executive with disturbance handling ability. Development of such an executive is challenging because the biped is highly nonlinear and has limited actuation due to its limited base of support. We address these challenges with three key innovations.(cont.) To address the nonlinearity, we develop a dynamic virtual model controller to linearize the biped, and thus, provide an abstracted biped that is easier to control. The controller is model-based, but uses a sliding control technique to compensate for model inaccuracy. To address the under-actuation, our system generates flow tubes, which define valid operating regions in the abstracted biped. The flow tubes represent sets of state trajectories that take into account dynamic limitations due to under-actuation, and also satisfy plan requirements. The executive keeps trajectories in the flow tubes by adjusting a small number of control parameters for key state variables in the abstracted biped, such as center of mass. Additionally, our system uses a novel strategy that employs angular momentum to enhance translational controllability of the system's center of mass. We evaluate our approach using a high-fidelity biped simulation. Tests include walking with foot-placement constraints, kicking a soccer ball, and disturbance recovery.by Andreas G. Hofmann.Ph.D

Robust Execution of Bipedal Walking Tasks From Biomechanical Principles

PhD thesisEffective use of robots in unstructured environments requires that they have sufficient autonomy and agility to execute task-level commands successfully. A challenging example of such a robot is a bipedal walking machine. Such a robot should be able to walk to a particular location within a particular time, while observing foot placement constraints, and avoiding a fall, if this is physically possible. Although stable walking machines have been built, the problem of task-level control, where the tasks have stringent state-space and temporal requirements, and where significant disturbances may occur, has not been studied extensively. This thesis addresses this problem through three objectives. The first is to devise a plan specification where task requirements are expressed in a qualitative form that provides for execution flexibility. The second is to develop a task-level executive that accepts such a plan, and outputs a sequence of control actions that result in successful plan execution. The third is to provide this executive with disturbance handling ability.Development of such an executive is challenging because the biped is highly nonlinear and has limited actuation due to its limited base of support. We address these challenges with three key innovations. To address the nonlinearity, we develop a dynamic virtual model controller to linearize the biped, and thus, provide an abstracted biped that is easier to control. The controller is model-based, but uses a sliding control technique to compensate for model inaccuracy. To address the under-actuation, our system generates flow tubes, which define valid operating regions in the abstracted biped. The flow tubes represent sets of state trajectories that take into account dynamic limitations due to under-actuation, and also satisfy plan requirements. The executive keeps trajectories in the flow tubes by adjusting a small number of control parameters for key state variables in the abstracted biped, such as center of mass. Additionally, our system uses a novel strategy that employs angular momentum to enhance translational controllability of the systemÂs center of mass. We evaluate our approach using a high-fidelity biped simulation. Tests include walking with foot-placement constraints, kicking a soccer ball, and disturbance recovery

Redesign for energy and reserve markets in electric power networks with high solar penetration

Favorable price trends and increasing demand for renewable energy sources portend accelerating integration of solar photovoltaic (PV) generation into traditional electric power system networks. Managing the variable output of massive PV resources makes system frequency regulation more complex and expensive. ISOs must procure additional regulation and load following capacity, while power plants must supply more regulation work. In contrast to costly physical storage solutions, this thesis proposes to address the issue by reconfiguring the electricity market pricing structure to translate all power imbalances into real-time market price signals. More accurately determining the instantaneous value of energy, electric power markets could reward participants who can quickly respond to frequency fluctuations. By utilizing short term forward markets to monetize the risk associated with intermittency, the true cost of reliability is determined and could reduce wasteful capacity payments. This market redesign is an ideal open platform for disparate smart grid technologies which could encourage all suppliers, loads and generator, to offer supply or reduce consumption when it is needed most and could vastly improve frequency performance metrics.MSCommittee Chair: Grijalva, Santiago; Committee Member: Begovic, Miroslav; Committee Member: Rohatgi, Ajee

Coordinating Agile Systems through the Model-based Execution of Temporal Plans

SM thesisAgile autonomous systems are emerging, such as unmanned aerial vehicles (UAVs), that must robustly perform tightly coordinated time-critical missions; for example, military surveillance or search-and-rescue scenarios. In the space domain, execution of temporally flexible plans has provided an enabler for achieving the desired coordination and robustness, in the context of space probes and planetary rovers, modeled as discrete systems. We address the challenge of extending plan execution to systems with continuous dynamics, such as air vehicles and robot manipulators, and that are controlled indirectly through the setting of continuous state variables.Systems with continuous dynamics are more challenging than discrete systems, because they require continuous, low-level control, and cannot be controlled by issuing simple sequences of discrete commands. Hence, manually controlling these systems (or plants) at a low level can become very costly, in terms of the number of human operators necessary to operate the plant. For example, in the case of a fleet of UAVs performing a search-and-rescue scenario, the traditional approach to controlling the UAVs involves providing series of close waypoints for each aircraft, which incurs a high workload for the human operators, when the fleet consists of a large number of vehicles.Our solution is a novel, model-based executive, called Sulu, that takes as input a qualitative state plan, specifying the desired evolution of the state of the system. This approach elevates the interaction between the human operator and the plant, to a more abstract level where the operator is able to Âcoach the plant by qualitatively specifying the tasks, or activities, the plant must perform. These activities are described in a qualitative manner, because they specify regions in the plantÂs state space in which the plant must be at a certain point in time. Time constraints are also described qualitatively, in the form of flexible temporal constraints between activities in the state plan. The design of low-level control inputs in order to meet this abstract goal specification is then delegated to the autonomous controller, hence decreasing the workload per human operator. This approach also provides robustness to the executive, by giving it room to adapt to disturbances and unforeseen events, while satisfying the qualitative constraints on the plant state, specified in the qualitative state plan.Sulu reasons on a model of the plant in order to dynamically generate near-optimal control sequences to fulfill the qualitative state plan. To achieve optimality and safety, Sulu plans into the future, framing the problem as a disjunctive linear programming problem. To achieve robustness to disturbances and maintain tractability, planning is folded within a receding horizon, continuous planning and execution framework. The key to performance is a problem reduction method based on constraint pruning. We benchmark performance using multi-UAV firefighting scenarios on a real-time, hardware-in-the-loop testbed

Compiling Away Uncertainty in Strong Temporal Planning with Uncontrollable Durations

Real world temporal planning often involves dealing with uncertainty about the duration of actions. In this paper, we describe a sound-and-complete compilation technique for strong planning that reduces any planning instance with uncertainty in the duration of actions to a plain temporal planning problem without uncertainty. We evaluate our technique by comparing it with a recently-presented technique for PDDL domains with temporal uncertainty. The experimental results demonstrate the practical applicability of our approach and show a complementary behavior with respect to the previous techniques. We also demonstrate the high expressiveness of the translation by applying it to a significant fragment of the ANML language