William D. Smart

ICRA Conference 2023 Conference Paper

Contextual Multi-Objective Path Planning

• Anna Nickelson
• Kagan Tumer
• William D. Smart

Many critical robot environments, such as healthcare and security, require robots to account for contextdependent criteria when performing their functions (e. g. , navigation). Such domains require decisions that balance multiple factors, making it difficult for robots to make contextually appropriate decisions. Multi-Objective Optimization (MOO) methods offer a potential solution by trading off between objectives; however concepts like Pareto fronts are not only expensive to compute but struggle with differentiating among solutions on the Pareto front. This work introduces the Contextual Multi-Objective Path Planning (CMOPP) algorithm, which enables the robot to trade off different complex costs dependent on context. The key insight of this work is to separate the path planning and path cost estimation into two independent steps, thus significantly reducing computation cost without impacting the quality of the resulting path. As a result, CMOPP is able to accurately model path costs, which provide meaningful trade-offs when choosing a path that best fits the context. We show the benefits of CMOPP on case studies that demonstrate its contextual path planning capabilities. CMOPP finds contextually appropriate paths by first reducing the search space up to 99. 9% to a near-optimal set of paths. This reduction enables the generation of accurate path cost models, using up to 90% less computation than similar methods.

IROS Conference 2017 Conference Paper

Neural networks for incremental dimensionality reduced reinforcement learning

• William Curran
• Rey Pocius
• William D. Smart

State-of-the-art personal robots must perform complex manipulation tasks to be viable in assistive scenarios. However, many of these robots, like the PR2, use manipulators with high degrees-of-freedom. The complexity of these robots lead to large dimensional state spaces, which are difficult to fully explore. Our previous work introduced the IDRRL algorithm, which compresses the learning space by transforming a high-dimensional learning space onto a lower-dimensional manifold while preserving expressivity. In this work we formally prove that IDRRL maintains PAC-MDP guarantees. We then improve upon our previous formulation of IDRRL by introducing cascading autoencoders (CAE) for dimensionality reduction, producing the new algorithm IDRRL-CAE. We demonstrate the improvement of this extension over our previous formulation, IDRRL-PCA, in the Mountain Car and Swimmers domains.

IROS Conference 2016 Conference Paper

Real-time contamination modeling for robotic health care support

• Kory Kraft
• Tiffany Chu
• Patrick Hansen
• William D. Smart

Real-time contamination monitoring in health care facilities would allow medical teams to take appropriate actions to carefully enter, avoid, or decontaminate contaminated areas, reducing infection risk for themselves and their patients. In this paper, we demonstrate and evaluate the first end-to-end, real-time contamination tracking system for robotic health care support. The system models contamination of the environment and people, directs decontamination efforts of a simulated scrubber robot, and alerts users when nearing contaminated areas. We outline our transmission model design choices, as based on Ebola virus disease, and evaluate the system against the spread of a physical substance.

ICRA Conference 2015 Conference Paper

Evaluating impact in the ROS ecosystem

• William Curran
• Thomas Thornton
• Benjamin Arvey
• William D. Smart

The ROS ecosystem is an interconnected web of packages, nodes and people with no efficient means to compare, assess or visualize them. We develop a set of tools consisting of various metrics, a data visualization web app, and an active monitoring system. With these tools, we measure the current state of the ecosystem as well as determine where the community should direct their efforts. We also encourage the community to provide input on potential applications, additional metrics, and further improvements to address the needs of the ROS ecosystem. We incentivize this input by gamifying community contributions to the infrastructure. Encouraging user-driven improvements to the ROS infrastructure through the use of a leaderboard and friendly competition will advance ROS development and community support far into the future.

IROS Conference 2014 Conference Paper

Layered costmaps for context-sensitive navigation

• David V. Lu
• Dave Hershberger
• William D. Smart

Many navigation systems, including the ubiquitous ROS navigation stack, perform path-planning on a single costmap, in which the majority of information is stored in a single grid. This approach is quite successful at generating collision-free paths of minimal length, but it can struggle in dynamic, people-filled environments when the values in the costmap expand beyond occupied or free space. We have created and implemented a new method called layered costmaps, which work by separating the processing of costmap data into semantically-separated layers. Each layer tracks one type of obstacle or constraint, and then modifies a master costmap which is used for the path planning. We show how the algorithm can be integrated with the open-source ROS navigation stack, and how our approach is easier to fine-tune to specific environmental contexts than the existing monolithic one. Our design also results in faster path planning in practical use, and exhibits a cleaner separation of concerns that the original architecture. The new algorithm also makes it possible to represent complex cost values in order to create navigation behavior for a wide range of contexts.

IROS Conference 2013 Conference Paper

Towards more efficient navigation for robots and humans

• David V. Lu
• William D. Smart

Effective robot navigation in the presence of humans is hard. Not only do human obstacles move, they react to the movements of the robot according to instinct and social rules. In order to efficiently navigate around each other, both the robot and the human must move in a way that takes the other into account. Failure to do so can lead to a lowering of the perceived quality of the interaction and, more importantly, it can also delay one or both parties, causing them to be less efficient in whatever task they are trying to achieve. In this paper, we present a system capable of creating more efficient corridor navigation behaviors by manipulating existing navigation algorithms and introducing social cues from the robot to the human. We give the results of a user study, demonstrating the effectiveness of our system, and discuss how it can be applied more generally to a wide variety of situations.

IROS Conference 2012 Conference Paper

Robots for humanity: User-centered design for assistive mobile manipulation

• Tiffany L. Chen
• Matei Ciocarlie
• Steve B. Cousins
• Phillip M. Grice
• Kelsey P. Hawkins
• Kaijen Hsiao
• Charles C. Kemp
• Chih-Hung King

The Robots for Humanity project aims to enable people with severe motor impairments to interact with their own bodies and their environment through the use of an assistive mobile manipulator, thereby improving their quality of life. Assistive mobile manipulators (AMMs) are mobile robots that physically manipulate the world in order to provide assistance to people with disabilities. They present an exciting frontier for assistive technology, as they can operate away from the user, have a large dexterous workspace (due to their mobility), and not directly encumber their users. The cornerstone of this project is an ongoing, interactive design process with a quadriplegic user, Henry Evans, and his wife and primary caregiver, Jane Evans. Henry has been enabled, through the use of a PR2 robot, to scratch his own face, shave, fetch a towel from his kitchen, and hand out Halloween candy to trick-ortreating children at a local mall.

ICRA Conference 2011 Conference Paper

An inexpensive robot platform for teleoperation and experimentation

• Daniel A. Lazewatsky
• William D. Smart

Most commercially-available robots are either aimed at the research community, or are designed with a single purpose in mind. The extensive hobbyist community has tended to focus on the hardware and the low-level software aspects. We claim that there is a need for a low-cost, general-purpose robot, accessible to the hobbyist community, with sufficient computation and sensing to run “research-grade” software. In this paper, we describe the design and implementation of such a robot. We explicitly outline our design goals, and show how a capable robot can be assembled from off-the-shelf parts, for a modest cost, by a single person with only a few tools. We also show how the robot can be used as a low-cost telepresence platform, giving the system a concrete purpose beyond being a low-cost development platform.

IROS Conference 2011 Conference Paper

Context-aware video compression for mobile robots

• Daniel A. Lazewatsky
• Bogumil Giertler
• Martha Witick
• Leah Perlmutter
• Bruce A. Maxwell
• William D. Smart

Operating robots across networks with unknown, bandwidth, latency and other conditions presents difficulty when the operation depends on real-time feedback and control. Standard video compression methods do a good job compressing arbitrary video, but do not take domain knowledge into account when more information about the video is known beforehand. We have incorporated robot odometry into the video pipeline, allowing video quality to be selectively reduced at times when odometry suggests that such a reduction will not adversely affect task performance of human operators. We found that selectively reducing video quality significantly reduced bandwidth usage, increasing the robot's responsiveness and controllability, while having no measurable effect on task performance.

UAI Conference 2010 Conference Paper

A Scalable Method for Solving High-Dimensional Continuous POMDPs Using Local Approximation

• Tom Erez
• William D. Smart

Partially-Observable Markov Decision Processes (POMDPs) are typically solved by finding an approximate global solution to a corresponding belief-MDP. In this paper, we offer a new planning algorithm for POMDPs with continuous state, action and observation spaces. Since such domains have an inherent notion of locality, we can find an approximate solution using local optimization methods. We parameterize the belief distribution as a Gaussian mixture, and use the Extended Kalman Filter (EKF) to approximate the belief update. Since the EKF is a first-order filter, we can marginalize over the observations analytically. By using feedback control and state estimation during policy execution, we recover a behavior that is effectively conditioned on incoming observations despite the unconditioned planning. Local optimization provides no guarantees of global optimality, but it allows us to tackle domains that are at least an order of magnitude larger than the current state-of-the-art. We demonstrate the scalability of our algorithm by considering a simulated hand-eye coordination domain with 16 continuous state dimensions and 6 continuous action dimensions.

UAI Conference 2010 Conference Paper

Real-Time Scheduling via Reinforcement Learning

• Robert Glaubius
• Terry Tidwell
• Christopher D. Gill
• William D. Smart

Cyber-physical systems, such as mobile robots, must respond adaptively to dynamic operating conditions. Effective operation of these systems requires that sensing and actuation tasks are performed in a timely manner. Additionally, execution of mission specific tasks such as imaging a room must be balanced against the need to perform more general tasks such as obstacle avoidance. This problem has been addressed by maintaining relative utilization of shared resources among tasks near a user-specified target level. Producing optimal scheduling strategies requires complete prior knowledge of task behavior, which is unlikely to be available in practice. Instead, suitable scheduling strategies must be learned online through interaction with the system. We consider the sample complexity of reinforcement learning in this domain, and demonstrate that while the problem state space is countably infinite, we may leverage the problem’s structure to guarantee efficient learning.

IROS Conference 2007 Conference Paper

Bipedal walking on rough terrain using manifold control

• Tom Erez
• William D. Smart

This paper presents an algorithm for adapting periodic behavior to gradual shifts in task parameters. Since learning optimal control in high dimensional domains is subject to the 'curse of dimensionality', we parametrize the policy only along the limit cycle traversed by the gait, and thus focus the computational effort on a closed one-dimensional manifold, embedded in the high-dimensional state space. We take an initial gait as a departure point, and iterate between modifying the task slightly, and adapting the gait to this modification. This creates a sequence of gaits, each optimized for a different variant of the task. Since every two gaits in this sequence are very similar, the whole sequence spans a two-dimensional manifold, and combining all policies in this 2-manifold provides additional robustness to the system. We demonstrate our approach on two simulations of bipedal robots - the compass gait walker, which is a four-dimensional system, and RABBIT, which is ten-dimensional. The walkers' gaits are adapted to a sequence of changes in the ground slope, and when all policies in the sequence are combined, the walkers can safely traverse a rough terrain, where the incline changes at every step.

IROS Conference 2007 Conference Paper

Faster and more accurate face detection on mobile robots using geometric constraints

• Michael Dixon
• Frederick Heckel
• Robert Pless
• William D. Smart

We develop a framework to allow generic object detection algorithms to exploit geometric information commonly available to robot vision systems. Robot systems take pictures with calibrated cameras from known positions and may simultaneously capture depth measurements in the scene. This allows known constraints on the 3D size and position of objects to be translated into constraints on potential locations and scales of objects in the image, eliminating potentially expensive image operations for geometrically infeasible object locations. We show this integration to be very natural in the context of face detection and find that the computational effort of the standard Viola Jones face detector (as implemented in OpenCV) can be reduced by 85 percent with three times fewer false positives.

IROS Conference 2006 Conference Paper

A Video Game-Based Mobile Robot Simulation Environment

• Josh Faust
• Cheryl Simon
• William D. Smart

Simulation is becoming an increasingly important aspect of mobile robots. As we are better able to simulate the real world, we can usefully perform more research in simulated environments. The key aspects of a good simulator, an accurate physics simulation and realistic graphical rendering system, are also central to modern computer games. In this paper, we describe a robot simulation environment built from technologies typically used in computer video games. The simulator is capable of simulating multiple robots, with realistic physics and rendering. It can also support human-controlled avatars using a traditional first-person interface. This allows us to perform robot-human interaction and collaboration studies in the simulated environment. The distributed nature of the simulation allows us to perform large-scale experiments, with users participating from geographically remote locations

ICML Conference 2004 Conference Paper

Interpolation-based Q-learning

• Csaba Szepesvári
• William D. Smart

ICRA Conference 2004 Conference Paper

The Remote Exploration Program: a Collaborative Outreach Approach to Robotics Education

• Jim Garner
• William D. Smart
• Keith Bennett
• David J. Bruemmer
• Douglas A. Few
• Christine M. Roman

High-school robotics competitions of all sorts are hugely popular. Robotics courses are now being widely taught in colleges and universities. Commercial applications of robotics are growing, as it becomes practical to deploy autonomous systems in the real world. Despite the activity in each of these areas, there is a lack of continuity between them. In this paper, we describe our plans for a novel outreach and education program that attempts to bridge between the worlds of high-school, university, and research laboratory robotics.

IROS Conference 2003 Conference Paper

An autonomous robot photographer

• Zachary Byers
• Michael Dixon
• Kevin Goodier
• Cindy M. Grimm
• William D. Smart

We describe a complete, end-to-end system for taking well-composed photographs using a mobile robot. The general scenario is a reception, or other event, where people are roaming around talking to each other. The robot serves as an "event photographer", roaming around the same space as the participants, periodically taking photographs. These images are then sent to a workstation where participants can print the photographs out, or email them.

ICRA Conference 2002 Conference Paper

Effective Reinforcement Learning for Mobile Robots

• William D. Smart
• Leslie Pack Kaelbling

Programming mobile robots can be a long, time-consuming process. Specifying the low-level mapping from sensors to actuators is prone to programmer misconceptions, and debugging such a mapping can be tedious. The idea of having a robot learn how to accomplish a task, rather than being told explicitly, is an appealing one. It seems easier and much more intuitive for the programmer to specify what the robot should be doing, and to let it learn the fine details of how to do it. In this paper, we introduce a framework for reinforcement learning on mobile robots and describe our experiments using it to learn simple tasks.

ICML Conference 2000 Conference Paper

Practical Reinforcement Learning in Continuous Spaces

• William D. Smart
• Leslie Pack Kaelbling

AAAI Conference 1998 Short Paper

A Framework for Reinforcement Learning on Real Robots

• William D. Smart