Deep Visual Foresight for Planning Robot Motion

A key challenge in scaling up robot learning to many skills and environments is removing the need for human supervision, so that robots can collect their own data and improve their own performance without being limited by the cost of requesting human feedback. Model-based reinforcement learning holds the promise of enabling an agent to learn to predict the effects of its actions, which could provide flexible predictive models for a wide range of tasks and environments, without detailed human supervision. We develop a method for combining deep action-conditioned video prediction models with model-predictive control that uses entirely unlabeled training data. Our approach does not require a calibrated camera, an instrumented training set-up, nor precise sensing and actuation. Our results show that our method enables a real robot to perform nonprehensile manipulation -- pushing objects -- and can handle novel objects not seen during training.Comment: ICRA 2017. Supplementary video: https://sites.google.com/site/robotforesight

An architecture for autonomic web service process planning

Web service composition is a technology that has received considerable attention in the last number of years. Languages and tools to aid in the process of creating composite web services have been received specific attention. Web service composition is the process of linking single web services together in order to accomplish more complex tasks. One area of web service composition that has not received as much attention is the area of dynamic error handling and re-planning, enabling autonomic composition. Given a repository of service descriptions and a task to complete, it is possible for AI planners to automatically create a plan that will achieve this goal. If however a service in the plan is unavailable or erroneous the plan will fail. Motivated by this problem, this paper suggests autonomous re-planning as a means to overcome dynamic problems. Our solution involves automatically recovering from faults and creating a context-dependent alternate plan

Domain specific software design for decision aiding

McDonnell Aircraft Company (MCAIR) is involved in many large multi-discipline design and development efforts of tactical aircraft. These involve a number of design disciplines that must be coordinated to produce an integrated design and a successful product. Our interpretation of a domain specific software design (DSSD) is that of a representation or framework that is specialized to support a limited problem domain. A DSSD is an abstract software design that is shaped by the problem characteristics. This parallels the theme of object-oriented analysis and design of letting the problem model directly drive the design. The DSSD concept extends the notion of software reusability to include representations or frameworks. It supports the entire software life cycle and specifically leads to improved prototyping capability, supports system integration, and promotes reuse of software designs and supporting frameworks. The example presented in this paper is the task network architecture or design which was developed for the MCAIR Pilot's Associate program. The task network concept supported both module development and system integration within the domain of operator decision aiding. It is presented as an instance where a software design exhibited many of the attributes associated with DSSD concept

NASA/NBS (National Aeronautics and Space Administration/National Bureau of Standards) standard reference model for telerobot control system architecture (NASREM)

The document describes the NASA Standard Reference Model (NASREM) Architecture for the Space Station Telerobot Control System. It defines the functional requirements and high level specifications of the control system for the NASA space Station document for the functional specification, and a guideline for the development of the control system architecture, of the 10C Flight Telerobot Servicer. The NASREM telerobot control system architecture defines a set of standard modules and interfaces which facilitates software design, development, validation, and test, and make possible the integration of telerobotics software from a wide variety of sources. Standard interfaces also provide the software hooks necessary to incrementally upgrade future Flight Telerobot Systems as new capabilities develop in computer science, robotics, and autonomous system control

The Role of Human-Automation Consensus in Multiple Unmanned Vehicle Scheduling

Objective: This study examined the impact of increasing automation replanning rates on operator performance and workload when supervising a decentralized network of heterogeneous unmanned vehicles. Background: Futuristic unmanned vehicles systems will invert the operator-to-vehicle ratio so that one operator can control multiple dissimilar vehicles connected through a decentralized network. Significant human-automation collaboration will be needed because of automation brittleness, but such collaboration could cause high workload. Method: Three increasing levels of replanning were tested on an existing multiple unmanned vehicle simulation environment that leverages decentralized algorithms for vehicle routing and task allocation in conjunction with human supervision. Results: Rapid replanning can cause high operator workload, ultimately resulting in poorer overall system performance. Poor performance was associated with a lack of operator consensus for when to accept the automation’s suggested prompts for new plan consideration as well as negative attitudes toward unmanned aerial vehicles in general. Participants with video game experience tended to collaborate more with the automation, which resulted in better performance. Conclusion: In decentralized unmanned vehicle networks, operators who ignore the automation’s requests for new plan consideration and impose rapid replans both increase their own workload and reduce the ability of the vehicle network to operate at its maximum capacity. Application: These findings have implications for personnel selection and training for futuristic systems involving human collaboration with decentralized algorithms embedded in networks of autonomous systems.Aurora Flight Sciences Corp.United States. Office of Naval Researc

Autonomous Search and Rescue with Modeling and Simulation and Metrics

Unmanned Aerial Vehicles (UAVs) provide rapid exploration capabilities in search and rescue missions while accepting more risks than human operations. One limitation in that current UAVs are heavily manpower intensive and such manpower demands limit abilities to expand UAV use. In operation, manpower demands in UAVs range from determining tasks, selecting waypoints, manually controlling platforms and sensors, and tasks in between. Often, even a high level of autonomy is possible with human generated objectives and then autonomous resource allocation, routing, and planning. However, manually generating tasks and scenarios is still manpower intensive. To reduce manpower demands and move towards more autonomous operations, the authors develop an adaptive planning system that takes high level goals from a human operator and translates them into situationally relevant tasking. For expository simulation, the authors further describe constructing a scenario around the 2018 Hawaii Puna lava natural disaster