Althea: Recalled Process For Planning and Execution (original) (raw)

Assumptive planning and execution: A simple, working robot architecture

Autonomous Robots, 1996

In this paper, we present a simple approach to interleaving planning and execution based on the idea of making simplifying assumptions. Since assumptions can be tragically wrong, this assumptive approach must ensure both that the robot does not believe it has solved a problem when it has not and that it does not take steps that make a problem unsolvable. We present an assumptive algorithm that preserves goal-reachability and in addition we specify conditions under which the assumptive architecture is sound and complete. We have successfully implemented the assumptive architecture on several real-world robots. Students in an introductory robot lab at Stanford University implement an assumptive system on robots that have incomplete information about their maze world. Dervish, our winning entry in the 1994 AAAI National Robgt Competition, implements an assumptive architecture to cope with partially specified environments and unreliable effecters and sensors.

Plan Representation for Robotic Agents

Plan-Based Control of Robotic Agents, 2002

This paper invents symbolic pattern databases (SPDB) to combine two influencing aspects for recent progress in domain-independent action planning, namely heuristic search and model checking. SPDBs are off-line computed dictionaries, generated in symbolic backward traversals of automatically inferred planning space abstractions. The entries of SPDBs serve as heuristic estimates to accelerate explicit and symbolic, approximate and optimal heuristic search planners. Selected experiments highlight that the symbolic representation yields much larger and more accurate pattern databases than the ones generated with explicit methods.

A framework for plan execution in behavior-based robots

Proceedings of the 1998 IEEE International Symposium on Intelligent Control (ISIC) held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) Intelligent Systems and Semiotics (ISAS) (Cat. No.98CH36262), 1998

This paper presents a conceptual architecture for autonomous robots that integrates behaviour-based and goal-directed action as by following a traditional action plan. Dual Dynamics is the formalism for describing behaviourbased action. Partial-order propositional plans, which get generated by GRAPHPLAN , are used as a basis for acting goal-directedly; the concept is suitable for using other planning methods and plan representations, though. The paper presents the corresponding action and plan representations at the plan side and at the behaviour side. Moreover, it describes how behaviour-based action is biased towards executing a plan and how information from the behaviour side is fed back to the plan side to determine progress in the plan execution.

A unified framework for planning and execution-monitoring of mobile robots

2011

We present an original integration of high level planning and execution with incoming perceptual information from vision, SLAM, topological map segmentation and dialogue. The task of the robot system, implementing the integrated model, is to explore unknown areas and report detected objects to an operator, by speaking loudly. The knowledge base of the planner maintains a graph-based representation of the metric map that is dynamically constructed via an unsupervised topological segmentation method, and augmented with information about the type and position of detected objects, within the map, such as cars or containers. According to this knowledge the cognitive robot can infer strategies in so generating parametric plans that are instantiated from the perceptual processes. Finally, a model-based approach for the execution and control of the robot system is proposed to monitor, concurrently, the low level status of the system and the execution of the activities, in order to achieve the goal, instructed by the operator.

Generation and execution of partially correct plans in dynamic environments

2002

In this paper we present the recent developments of the approach to the design of Cognitive Robots (i.e. robots whose actions are driven by a formally developed theory of action), that are capable of performing tasks in a coordinated way. The logic of actions that we adopt is an epistemic dynamic logic, where it is possible to derive acyclic branching plans (branches corresponding to sensing actions), including primitive parallel actions. In the present work, we consider an extended notion of plan by admitting a simple class of cycles that arise from the attempt to recover from the failure states originated by sensing actions. The proposed extension allows us to address the problem of generating plans that handle a form of synchronization based on the recognition of specific situations through sensing actions, including forms of coordination required in a multi-robot scenario.

Symbolic Probabilistic-Conditional Plans Execution by a Mobile Robot

Reasoning with Uncertainty in Robotics, 2005

In this paper we report on the integration of a high-level plan executor with a behavior-based architec- ture. The executor is designed to execute plans that solve problems in partially observable domains. We discuss the different modules of the overall architecture and how we made the different modules interact using a shared representation. We also give a detailed description of

HiPPo: Hierarchical POMDPs for Planning Information Processing and Sensing Actions on a Robot

Flexible general purpose robots need to tailor their visual pro- cessing to their task, on the fly. We propose a new approach to this within a planning framework, where the goal is to plan a sequence of visual operators to apply to the regions of interest (ROIs) in a scene. We pose the visual processing problem as a Partially Observable Markov Decision Process (POMDP). This requires probabilistic models of operator effects to quan- titatively capture the unreliability of the processing actions, and thus reason precisely about trade-offs between plan ex- ecution time and plan reliability. Since planning in practical sized POMDPs is intractable we show how to ameliorate this intractability somewhat for our domain by defining a hier- archical POMDP. We compare the hierarchical POMDP ap- proach with a Continual Planning (CP) approach. On a real robot visual domain, we show empirically that all the plan- ning methods outperform naive application of all visual op- erators. The key result ...

Plan explicability and predictability for robot task planning

2017 IEEE International Conference on Robotics and Automation (ICRA), 2017

Intelligent robots and machines are becoming pervasive in human populated environments. A desirable capability of these agents is to respond to goal-oriented commands by autonomously constructing task plans. However, such autonomy can add significant cognitive load and potentially introduce safety risks to humans when agents behave unexpectedly. Hence, for such agents to be helpful, one important requirement is for them to synthesize plans that can be easily understood by humans. While there exists previous work that studied socially acceptable robots that interact with humans in "natural ways", and work that investigated legible motion planning, there lacks a general solution for high level task planning. To address this issue, we introduce the notions of plan explicability and predictability. To compute these measures, first, we postulate that humans understand agent plans by associating abstract tasks with agent actions, which can be considered as a labeling process. We learn the labeling scheme of humans for agent plans from training examples using conditional random fields (CRFs). Then, we use the learned model to label a new plan to compute its explicability and predictability. These measures can be used by agents to proactively choose or directly synthesize plans that are more explicable and predictable to humans. We provide evaluations on a synthetic domain and with human subjects using physical robots to show the effectiveness of our approach.