Jamieson Stewart C.

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Jamieson
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Stewart C.
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  • Thesis
    Enabling human-robot cooperation in scientific exploration of bandwidth-limited environments
    (Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2020-05) Jamieson, Stewart C. ; Girdhar, Yogesh ; How, Jonathan P.
    Contemporary scientific exploration most often takes place in highly remote and dangerous environments, such as in the deep sea and on other planets. These environments are very hostile to humans, which makes robotic exploration the first and often the only option. However, they also impose restrictive limits on how much communication is possible, creating challenges in implementing remote command and control. We propose an approach to enable more efficient autonomous robot-based scientific exploration of remote environments despite these limits on human-robot communication. We find this requires the robot to have a spatial observation model that can predict where to find various phenomena, a reward model which can measure how relevant these phenomena are to the scientific mission objectives, and an adaptive path planner which can use this information to plan high scientific value paths. We identified and addressed two main gaps: the lack of a general-purpose means for spatial observation modelling, and the challenge in learning a reward model based on images online given the limited bandwidth constraints. Our first key contribution is enabling general-purpose spatial observation modelling through spatio-temporal topic models, which are well suited for unsupervised scientific exploration of novel environments. Our next key contribution is an active learning criterion which enables learning an image-based reward model during an exploration mission by communicating with the science team efficiently. We show that using these together can result in a robotic explorer collecting up to 230% more scientifically relevant observations in a single mission than when using lawnmower trajectories.
  • Thesis
    Enabling human-multi-robot collaborative visual exploration in underwater environments
    (Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2024-05) Jamieson, Stewart C. ; Girdhar, Yogesh
    This thesis presents novel approaches to vision-based autonomous exploration in underwater environments using human-multi-robot systems, enabling robots to adapt to evolving mission priorities learned via a human supervisor’s responses to images collected in situ. The robots model the spatial distribution of various habitats and terrain types in the environment using semantic classes learned online, and send image queries to the supervisor to learn which of these classes are associated with the highest concentration of targets of interest. The robots do not require prior examples of these targets, and learn these concentration parameters online. This approach is suitable for exploration in unfamiliar environments where unexpected phenomena are frequently discovered, such as coral reefs. A novel risk-based online learning algorithm identifies the concentration parameters using the fewest possible number of queries, enabling the robots to adapt quickly and reducing the operational burden on the supervisor. I introduce four primary contributions to address prevalent challenges in underwater exploration. Firstly, a multi-robot semantic representation matching algorithm enables interrobot sharing of semantic maps, generating consistent global maps with 20-60% higher quality scores than those produced by other methods. Next, we present DeepSeeColor, a novel real-time algorithm for correcting underwater image color distortions, which achieves up to 60 Hz processing speeds, thereby enabling improved semantic mapping and target recognition accuracy online. Thirdly, an efficient risk-based online learning algorithm ensures effective communication between robots and human supervisors, and, while remaining computationally tractable, overcomes the myopia which would cause previous algorithms to underestimate a query’s value. Lastly, we propose a new reward model and planning algorithm tailored for autonomous exploration, together enabling a 25-75% increase in the number of targets of interest located when compared to baseline surveys. These experiments were conducted with simulated robots exploring real coral reef maps and with real, ecologically meaningful targets of interest. Collectively, these contributions overcome key barriers to vision-based autonomous underwater exploration, and enhance the capability of autonomous underwater vehicles to adapt to new and evolving mission objectives in situ. Beyond marine exploration, these contributions have value in broader applications, such as space exploration, ecosystem monitoring, and other online learning problems.