Results for “autonomous platforms” — Page 26

NASA NTRS2005

An Overview of the Guided Parafoil System Derived from X-38 Experience

The NASA Johnson Space Center built a 4200 sq ft parafoil for the U.S. Army Natick Soldier Center to demonstrate autonomous flight using a guided parafoil system to deliver 10,000 lbs of useable payload. The parafoil's design was based upon that developed during the X-38 program. The drop test payload consisted of a standard 20-foot Type V airdrop platform, a standard 12-foot weight tub, a 60 ft drogue parachute, a 4200 ft2 parafoil, an instrumentation system, and a Guidance, Navigation, and Control (GN&C) system. Instrumentation installed on the load was used to gather data to validate simulation models and preflight loads predictions and to perform post flight trajectory and performance reconstructions. The GN&C system, developed during NASA's X-38 program, consisted of a flight computer, modems for uplink commands and downlink data, a compass, laser altimeter, and two winches. The winches were used to steer the parafoil and to perform the dynamic flare maneuver for a soft landing. The laser was used to initiate the flare. The GN&C software was originally provided to NASA by the European Space Agency. NASA incorporated further software refinements based upon the X-38 flight test results. Three full-scale drop tests were conducted, with the third being performed during the Precision Airdrop Technology Conference and Demonstration (PATCAD) Conference at the U.S. Army Yuma Proving Ground (YPG) in November of 2003. For the PATCAD demonstration, the parafoil and GN&C software and hardware performed well, concluding with a good flare and the smallest miss distance ever experienced in NASA's parafoil drop test program. This paper describes the 4200 sq ft parafoil system, simulation results, and the results of the drop tests.

Stein, Jenny M.↗

NASA NTRS2014

Ground Operations Autonomous Control and Integrated Health Management

The Ground Operations Autonomous Control and Integrated Health Management plays a key role for future ground operations at NASA. The software that is integrated into this system is called G2 2011 Gensym. The purpose of this report is to describe the Ground Operations Autonomous Control and Integrated Health Management with the use of the G2 Gensym software and the G2 NASA toolkit for Integrated System Health Management (ISHM) which is a Computer Software Configuration Item (CSCI). The decision rationale for the use of the G2 platform is to develop a modular capability for ISHM and AC. Toolkit modules include knowledge bases that are generic and can be applied in any application domain module. That way, there's a maximization of reusability, maintainability, and systematic evolution, portability, and scalability. Engine modules are generic, while application modules represent the domain model of a specific application. Furthermore, the NASA toolkit, developed since 2006 (a set of modules), makes it possible to create application domain models quickly, using pre-defined objects that include sensors and components libraries for typical fluid, electrical, and mechanical systems.

Computer Programs↗

NASA NTRS2016

Flight Analysis of an Autonomously Navigated Experimental Lander for High Altitude Recovery

First steps have been taken to qualify a family of parafoil systems capable of increasing the survivability and reusability of high-altitude balloon payloads. The research is motivated by the common risk facing balloon payloads where expensive flight hardware can often land in inaccessible areas that make them difficult or impossible to recover. The Autonomously Navigated Experimental Lander (ANGEL) flight test introduced a commercial Guided Parachute Aerial Delivery System (GPADS) to a previously untested environment at 108,000ft MSL to determine its high-altitude survivability and capabilities. Following release, ANGEL descended under a drogue until approximately 25,000ft, at which point the drogue was jettisoned and the main parachute was deployed, commencing navigation. Multiple data acquisition platforms were used to characterize the return-to-point technology performance and help determine its suitability for returning future scientific payloads ranging from 180 to 10,000lbs to safer and more convenient landing locations. This report describes the test vehicle design, and summarizes the captured sensor data. Various post-flight analyses are used to quantify the system's performance, gondola load data, and serve as a reference point for subsequent missions.

Parafoil↗

NASA NTRS2017

Exploring Concepts of Operations for On-Demand Passenger Air Transportation

In recent years, a surge of interest in "flying cars" for city commutes has led to rapid development of new technologies to help make them and similar on-demand mobility platforms a reality. To this end, this paper provides analyses of the stakeholders involved, their proposed operational concepts, and the hazards and regulations that must be addressed. Three system architectures emerged from the analyses, ranging from conventional air taxi to revolutionary fully autonomous aircraft operations, each with vehicle safety functions allocated differently between humans and machines. Advancements for enabling technologies such as distributed electric propulsion and artificial intelligence have had major investments and initial experimental success, but may be some years away from being deployed for on-demand passenger air transportation at scale.

Nneji, Victoria Chibuogu↗

NASA NTRS2007

X-Band Radar for Studies of Tropical Storms from High Altitude UAV Platform

The increased role of unmanned aerial vehicles (UAV) in NASA's suborbital program has created a strong interest in the development of instruments with new capabilities, more compact sizes and reduced weights than the instruments currently operated on manned aircrafts. There is a strong demand and tremendous potential for using high altitude UAV (HUAV) to carry weather radars for measurements of reflectivity and wind fields from tropical storms. Tropical storm genesis frequently occurs in ocean regions that are inaccessible to piloted aircraft due to the long off shore range and the required periods of time to gather significant data. Important factors of interest for the study of hurricane genesis include surface winds, profiled winds, sea surface temperatures, precipitation, and boundary layer conditions. Current satellite precipitation and surface wind sensors have resolutions that are too large and revisit times that are too infrequent to study this problem. Furthermore, none of the spaceborne sensors measure winds within the storm itself. A dual beam X-band Doppler radar, UAV Radar (URAD), is under development at the NASA Goddard Space Flight Center for the study of tropical storms from HUAV platforms, such as a Global Hawk. X-band is the most desirable frequency for airborne weather radars since these can be built in a relatively compact size using off-the-shelf components which cost significantly less than other higher frequency radars. Furthermore, X-band radars provide good sensitivity with tolerable attenuation in storms. The low-cost and light-weight URAD will provide new capabilities for studying hurricane genesis by analyzing the vertical structure of tropical cyclones as well as 3D reflectivity and wind fields in clouds. It will enable us to measure both the 3D precipitation structure and surface winds by using two antenna beams: fixed nadir and conical scanning each produced by its associated subsystem. The nadir subsystem is a magnetron based radar modified from a marine radar transceiver. It is capable of measuring vertical reflectivity and velocity profile while being a lower-cost, smaller size, and lighter weight version of the NASA ER-2 Doppler Radar (EDOP), which has flown during many NASA field campaigns and has provided valuable scientific information on hurricanes and weather phenomena. Unfortunately, EDOP is too large and heavy for most UAV platforms, but the experience gained with this instrument provided us with the heritage to build a new low-cost, light-weight, smaller system that will be capable of flying on UAVs. The scanning subsystem uses a TWT transmitter and provides measurements of 3D reflectivity/wind fields in-clouds. Conical scanning of the radar beam at a 35 deg. incidence angle will also provide information of surface wind speed and direction derived from the surface return over a single 360 deg. sweep. URAD data system will be Linux based with the capability of autonomous operation. It will utilize cutting edge digital receiver and FPGA technologies to carry out the data acquisition and processing tasks. High speed navigation data from the aircraft will also be captured and saved along with radar data for 3D measurement field reconstruction and aircraft motion correction. There is a tremendous potential for UAVs to carry down-looking weather radars for measurements of reflectivity, horizontal and vertical winds from tropical storms. With operation from HUAV platforms, the dual beam X-band radar under development promises to provide greatly needed information for tropical storm research.

Rodriquez, Shannon↗

NASA NTRS1994

Development of a Power Electronics Unit for the Space Station Plasma Contactor

A hollow cathode plasma contactor has been baselined as a charge control device for the Space Station (SS) to prevent deleterious interactions of coated structural components with the ambient plasma. NASA LeRC Work Package 4 initiated the development of a plasma contactor system comprised of a Power Electronics Unit (PEU), an Expellant Management Unit (EMU), a command and data interface, and a Plasma Contactor Unit (PCU). A breadboard PEU was designed and fabricated. The breadboard PEU contains a cathode heater and discharge power supply, which were required to operate the PCU, a control and auxiliary power converter, an EMU interface, a command and telemetry interface, and a controller. The cathode heater and discharge supplies utilized a push-pull topology with a switching frequency of 20 kHz and pulse-width-modulated (PWM) control. A pulse ignition circuit derived from that used in arcjet power processors was incorporated in the discharge supply for discharge ignition. An 8088 based microcontroller was utilized in the breadboard model to provide a flexible platform for controller development with a simple command/data interface incorporating a direct connection to SS Mulitplexer/Demultiplexer (MDM) analog and digital I/O cards. Incorporating this in the flight model would eliminate the hardware and software overhead associated with a 1553 serial interface. The PEU autonomously operated the plasma contactor based on command inputs and was successfully integrated with a prototype plasma contactor unit demonstrating reliable ignition of the discharge and steady-state operation.

Hamley, John A.↗

NASA NTRS1994

Controlling multiple security robots in a warehouse environment

The Naval Command Control and Ocean Surveillance Center (NCCOSC) has developed an architecture to provide coordinated control of multiple autonomous vehicles from a single host console. The multiple robot host architecture (MRHA) is a distributed multiprocessing system that can be expanded to accommodate as many as 32 robots. The initial application will employ eight Cybermotion K2A Navmaster robots configured as remote security platforms in support of the Mobile Detection Assessment and Response System (MDARS) Program. This paper discusses developmental testing of the MRHA in an operational warehouse environment, with two actual and four simulated robotic platforms.

Everett, H. R.↗

NASA NTRSDate not supplied

NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing

NASA’s Small Spacecraft & Distributed Systems (SSDS) within the Research and Technology Mission Directorate (RTMD) expands U.S. capability to execute unique missions through targeted investment, rapid development, and flight demonstration of small spacecraft technologies applicable to exploration, science and the commercial space sector. SSDS strategically invests in technology development and on-orbit demonstrations executed across NASA, other government agencies, industry, and academia. The program’s University SmallSat Technology Partnerships initiative awards academic researchers with the opportunity to collaborate with NASA to mature innovative technology. Capabilities aligned with RTMD’s technology shortfalls and interests - power, processing, propulsion, sensors, communications, autonomous navigation, architectures, and advanced applications like artificial intelligence (AI), machine learning, and edge computing - are prioritized in SSDS investments. These investments enable distributed, autonomous, and cooperative small spacecraft systems that support swarm missions extending beyond low Earth orbit into cislunar and deep space. This paper highlights representative SSDS flight demonstrations that mature these capabilities to enable a future operational infrastructure needed to support sustained exploration of the Moon and beyond. SSDS’s investment strategy emphasizes rapid development and on-orbit demonstration to validate spacecraft technologies required for swarms and distributed mission architectures. The Starling swarm technology demonstration mission exemplifies this approach by advancing distributed spacecraft autonomy, cooperative operations, and space situational awareness. Extended flight testing and ongoing studies of next generation swarm configurations and on-orbit space traffic monitoring and management continue to inform future swarm designs. DiskSat’s four-spacecraft demonstration mission represents SSDS’s strategic vision to expand the design space for future small spacecraft through its commitment to advance novel platform concepts that can impact how science is performed on orbit. Continuing to invest in future platforms, the notional PY12 concept is a 12-spacecraft swarm hosting neuromorphic processors and is envisioned as an on-orbit testbed for AI, edge computing, and positioning, navigation and timing technologies. SSDS also invests in single-spacecraft technology demonstrations that underpin the success of future swarm missions and accelerate the availability of validated technologies across the small spacecraft ecosystem. Examples of such demonstrations include Pathfinder Technology Demonstrator-3 (PTD-3), which performed high-rate optical communications; PTD-R, which demonstrated a camera capable of simultaneous ultraviolet and short-wave infrared optical sensing; and CAPSTONE, the Cislunar Autonomous Positioning System Technology and Operations Navigation Experiment, which validated autonomous navigation in cislunar space. Collectively, SSDS-funded demonstrations advance capabilities across swarms and illustrate a coordinated investment strategy to mature high-impact technologies required for autonomous, distributed, and cooperative small spacecraft systems for low Earth orbit, cislunar, and deep space applications. Technology demonstrations strengthen SSDS partnerships with industry, academia, and other government agencies, and promote small spacecraft community adoption of capabilities required to close technical gaps for swarm missions.

Jan Stupl↗

NASA NTRSDate not supplied

NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing

NASA’s Small Spacecraft & Distributed Systems (SSDS) within the Research and Technology Mission Directorate (RTMD) expands U.S. capability to execute unique missions through targeted investment, rapid development, and flight demonstration of small spacecraft technologies applicable to exploration, science and the commercial space sector. SSDS strategically invests in technology development and on-orbit demonstrations executed across NASA, other government agencies, industry, and academia. The program’s University SmallSat Technology Partnerships initiative awards academic researchers with the opportunity to collaborate with NASA to mature innovative technology. Capabilities aligned with RTMD’s technology shortfalls and interests - power, processing, propulsion, sensors, communications, autonomous navigation, architectures, and advanced applications like artificial intelligence (AI), machine learning, and edge computing - are prioritized in SSDS investments. These investments enable distributed, autonomous, and cooperative small spacecraft systems that support swarm missions extending beyond low Earth orbit into cislunar and deep space. This paper highlights representative SSDS flight demonstrations that mature these capabilities to enable a future operational infrastructure needed to support sustained exploration of the Moon and beyond. SSDS’s investment strategy emphasizes rapid development and on-orbit demonstration to validate spacecraft technologies required for swarms and distributed mission architectures. The Starling swarm technology demonstration mission exemplifies this approach by advancing distributed spacecraft autonomy, cooperative operations, and space situational awareness. Extended flight testing and ongoing studies of next generation swarm configurations and on-orbit space traffic monitoring and management continue to inform future swarm designs. DiskSat’s four-spacecraft demonstration mission represents SSDS’s strategic vision to expand the design space for future small spacecraft through its commitment to advance novel platform concepts that can impact how science is performed on orbit. Continuing to invest in future platforms, the notional PY12 concept is a 12-spacecraft swarm hosting neuromorphic processors and is envisioned as an on-orbit testbed for AI, edge computing, and positioning, navigation and timing technologies. SSDS also invests in single-spacecraft technology demonstrations that underpin the success of future swarm missions and accelerate the availability of validated technologies across the small spacecraft ecosystem. Examples of such demonstrations include Pathfinder Technology Demonstrator-3 (PTD-3), which performed high-rate optical communications; PTD-R, which demonstrated a camera capable of simultaneous ultraviolet and short-wave infrared optical sensing; and CAPSTONE, the Cislunar Autonomous Positioning System Technology and Operations Navigation Experiment, which validated autonomous navigation in cislunar space. Collectively, SSDS-funded demonstrations advance capabilities across swarms and illustrate a coordinated investment strategy to mature high-impact technologies required for autonomous, distributed, and cooperative small spacecraft systems for low Earth orbit, cislunar, and deep space applications. Technology demonstrations strengthen SSDS partnerships with industry, academia, and other government agencies, and promote small spacecraft community adoption of capabilities required to close technical gaps for swarm missions.

Jan Stupl↗

DOE OSTI2026

Author Correction: An autonomous laboratory for the accelerated synthesis of inorganic materials

Following publication of this article, concerns were raised about the unambiguous identification of the compound structures using diffraction as well as the original claims of material novelty. We acknowledge that the original claims of material novelty were subject to misinterpretation—their intention was to indicate that the materials were new to the prediction platform, not necessarily new to science. The article text has been updated to reflect this in the HTML and PDF versions of the article.

Szymanski, Nathan J. [University of California, Be↗

NASA NTRS2010

Micro/Nanoscale Chemicalsensor Systems for Aerospace Applications

The aerospace industry requires development of a range of chemical-sensor technologies for applications including emissions monitoring as well as fuel-leak and fire detection. Improvements in sensing technology are necessary to increase safety, reduce emissions, and increase performance. The overall aim is to develop intelligent-vehicle systems that can autonomously monitor their state and respond to environmental changes. A range of chemical sensors is under development to meet these needs, based in part on microfabrication technology which produces sensors of minimal size, weight, and power consumption. We have fabricated a range of sensor platforms, integrated them with hardware to form complete sensor systems, and demonstrated their applicability.

Hunter, Gary↗

NASA NTRS2016

Flight Analysis of an Autonomously Navigated Experimental Lander

First steps have been taken to qualify a family of parafoil systems capable of increasing the survivability and reusability of high-altitude balloon payloads. The research is motivated by the common risk facing balloon payloads where expensive flight hardware can often land in inaccessible areas that make them difficult or impossible to recover. The Autonomously Navigated Experimental Lander (ANGEL) flight test introduced a commercial Guided Parachute Aerial Delivery System (GPADS) to a previously untested environment at 108,000 feet Mean Sea Level (MSL) to determine its high-altitude survivability and capabilities. Following release, ANGEL descended under a drogue until approximately 25,000 feet, at which point the drogue was jettisoned and the main parachute was deployed, commencing navigation. Multiple data acquisition platforms were used to characterize the return-to-point technology performance and help determine its suitability for returning future scientific payloads ranging from 180 to 10,000 pounds to safer and more convenient landing locations. This report describes the test vehicle design, and summarizes the captured sensor data. Various post-flight analyses are used to quantify the systems performance, gondola load data, and serve as a reference point for subsequent missions.

Parafoil↗

NASA NTRS2003

The Logical Extension

The same software controlling autonomous and crew-assisted operations for the International Space Station (ISS) is enabling commercial enterprises to integrate and automate manual operations, also known as decision logic, in real time across complex and disparate networked applications, databases, servers, and other devices, all with quantifiable business benefits. Auspice Corporation, of Framingham, Massachusetts, developed the Auspice TLX (The Logical Extension) software platform to effectively mimic the human decision-making process. Auspice TLX automates operations across extended enterprise systems, where any given infrastructure can include thousands of computers, servers, switches, and modems that are connected, and therefore, dependent upon each other. The concept behind the Auspice software spawned from a computer program originally developed in 1981 by Cambridge, Massachusetts-based Draper Laboratory for simulating tasks performed by astronauts aboard the Space Shuttle. At the time, the Space Shuttle Program was dependent upon paper-based procedures for its manned space missions, which typically averaged 2 weeks in duration. As the Shuttle Program progressed, NASA began increasing the length of manned missions in preparation for a more permanent space habitat. Acknowledging the need to relinquish paper-based procedures in favor of an electronic processing format to properly monitor and manage the complexities of these longer missions, NASA realized that Draper's task simulation software could be applied to its vision of year-round space occupancy. In 1992, Draper was awarded a NASA contract to build User Interface Language software to enable autonomous operations of a multitude of functions on Space Station Freedom (the station was redesigned in 1993 and converted into the international venture known today as the ISS)

Source record↗

DOE OSTI2025

Automating ridehailing services would reduce pooling, especially among women

Here, this study investigates how autonomous vehicles (AVs) could transform pooled (shared) ridehailing services, focusing on the impacts of fare reductions, the absence of drivers/staff, and psychological attributes such as trust in other passengers and privacy concerns. We distinguish between the automation of driving tasks and the removal of human driver/staff from the vehicle, providing novel insights into the factors influencing AV ridehailing adoption. Using a national survey with stated preference (SP) choice experiments and psychometric questions, we analyze the complex interactions of ridehailing fare, pooled ridehailing service quality, and latent attitudes on ridehailing choices. Our findings suggest that the elimination of drivers/staff from fully autonomous ridehailing could lead to a shift from pooled to solo rides, particularly among female travelers who may have greater concerns about trust and safety in unstaffed AVs. This study highlights the importance of addressing trust and comfort beyond fare discounts to ensure the inclusivity and widespread adoption of pooled AV ridehailing. These insights underscore the need for ridehailing providers and policymakers to prioritize trust-building measures, user-centered AV design that offers greater privacy, and dynamic pricing strategies, to ensure inclusive and widespread adoption of pooled AV services.

Autonomous vehicle↗

DOE OSTI2026

Autonomous monitoring of algal biomass: Success stories and lessons learned from long-term field deployment

Autonomous, high-frequency monitoring of outdoor algal ponds is needed to quantify biomass productivity and detect culture decline in environments prone to contamination, grazers, and variable operating conditions. We report successes and lessons learned in translating a laboratory spectroradiometric monitoring approach to a multi-year autonomous field deployment at the Arizona Center for Algae Technology and Innovation (AzCATI). The system measures spectrally resolved pond reflectance by ratioing upwelling radiance from each raceway to simultaneous downwelling sky irradiance using fiber-coupled spectrometers. A physics-based reflectance model (ASHARP) is fit to each spectrum pair to estimate optical parameters, including a biomass-proxy coefficient (C a ) which enables near-real-time tracking of biomass accumulation and culture state at 2–5 min intervals. From May 2022 through September 2025 the platform operated continuously while scaling from two to six raceway ponds. Several strains of algae were monitored successfully, including the high productivity Tetraselmis striata and Picochlorum celeri. Transitioning data acquisition from a Windows laptop to a Raspberry Pi improved uptime from 57% (2022) to ~89% (2024–2025) and enabled routine real-time analysis. Further, we converted relative biomass estimates to absolute ash-free dry weight (AFDW) using experimentally-derived calibrations, providing field-relevant biomass predictions with conservative confidence bounds. These results demonstrate the feasibility of long-term, autonomous optical monitoring for well-mixed open-raceway algal cultivation and provide practical guidance for reliable field operation and scaling.

Katinas, Christopher Michael [Sandia National Labo↗

NASA NTRS2017

Spacecraft in the Shot: A Platform for Deep-Space Cinematography

Spacecraft In The Shot (SITS) is a breakthrough cross-disciplinary initiative that stands at the intersection of advanced technology and strategic storytelling. It aims to enhance humanity’s connection to robotic space exploration by capturing high-definition footage of heroic spacecraft along their journey through deep space. The SITS Imaging Instrument, a self-contained, highly autonomous instrument package can be included in any typical mission payload. The Instrument stores and deploys SITS imagers, or camera-carrying nanosats. As a mission approaches a key milestone in the trajectory, the SITS Instrument deploys one or more imagers that fly pre-programmed, autonomous trajectories around the host spacecraft. After the footage has been collected and downlinked to the host, the Imagers get decommissioned and left behind as the host spacecraft continues into the depths of space until the next photo opportunity.

Smith, Jonathon↗

DOE OSTI2025

OLCF’s Advanced Computing Ecosystem (ACE): FY25 Update for Ongoing Efforts

The advent of widespread use of artificial intelligence (AI) and machine learning (ML) models in science, coupled with fast data production rates of scientific instruments strain the traditional batch-oriented high-performance computing (HPC) environment. As scientific exploration continues to require more data and faster processing and analysis, new emerging technologies and capabilities to enable cross-facility and time-sensitive workflows are required for seamless integration of HPC and experimental facilities. The Advanced Computing Ecosystem (ACE) is a strategic initiative within the Oak Ridge Leadership Computing Facility (OLCF) established in 2024 to support the development of cutting-edge technologies to advance computational research and infrastructure at OLCF and across the Department of Energy (DOE). Several DOE initiatives are spearheading the evolution of the scientific landscape by blurring facility boundaries and connecting the user facilities to advance scientific capabilities and ensure energy dominance. The DOE Integrated Research Infrastructure (IRI) program is one example that is laying a foundation to support complex cross-facility workflows. The IRI program aims to integrate diverse computational resources, data infrastructures, and scientific instruments to facilitate collaboration and accelerate scientific discovery. The Interconnected Science Ecosystem (INTERSECT) initiative at Oak Ridge National Laboratory (ORNL) is another example that aims to revolutionize scientific research through AI-driven, interconnected autonomous laboratories and research facilities. Finally, the American Science Cloud (AmSC), recently announced in the “One Big Beautiful Bill”, aims to leverage prior infrastructure efforts of the IRI and automation and AI efforts of INTERSECT (and others) to build a federated, AI-augmented AmSC platform to unify the DOE’s computing, experimental, and data resources to catalyze scientific innovation.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2005

Predictive Interfaces for Long-Distance Tele-Operations

We address the development of predictive tele-operator interfaces for humanoid robots with respect to two basic challenges. Firstly, we address automating the transition from fully tele-operated systems towards degrees of autonomy. Secondly, we develop compensation for the time-delay that exists when sending telemetry data from a remote operation point to robots located at low earth orbit and beyond. Humanoid robots have a great advantage over other robotic platforms for use in space-based construction and maintenance because they can use the same tools as astronauts do. The major disadvantage is that they are difficult to control due to the large number of degrees of freedom, which makes it difficult to synthesize autonomous behaviors using conventional means. We are working with the NASA Johnson Space Center's Robonaut which is an anthropomorphic robot with fully articulated hands, arms, and neck. We have trained hidden Markov models that make use of the command data, sensory streams, and other relevant data sources to predict a tele-operator's intent. This allows us to achieve subgoal level commanding without the use of predefined command dictionaries, and to create sub-goal autonomy via sequence generation from generative models. Our method works as a means to incrementally transition from manual tele-operation to semi-autonomous, supervised operation. The multi-agent laboratory experiments conducted by Ambrose et. al. have shown that it is feasible to directly tele-operate multiple Robonauts with humans to perform complex tasks such as truss assembly. However, once a time-delay is introduced into the system, the rate of tele\ioperation slows down to mimic a bump and wait type of activity. We would like to maintain the same interface to the operator despite time-delays. To this end, we are developing an interface which will allow for us to predict the intentions of the operator while interacting with a 3D virtual representation of the expected state of the robot. The predictive interface anticipates the intention of the operator, and then uses this prediction to initiate appropriate sub-goal autonomy tasks.

Wheeler, Kevin R.↗