Low-shot learning methods for image classification support learning from sparse data. We extend these techniques to support dense semantic image segmentation. Specifically, we train a network that, given a small set of annotated images, produces parameters for a Fully Convolutional Network (FCN). We use this FCN to perform dense pixel-level prediction on a test image for the new semantic class. Our architecture shows a 25% relative meanIoU improvement compared to the best baseline methods for one-shot segmentation on unseen classes in the PASCAL VOC 2012 dataset and is at least 3× faster. The code is publicly available at: https://github.com/lzzcd001/OSLSM.

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A novel representation for the human component of multi-step, human-robot collaborative activity is presented. The goal of the system is to predict in a probabilistic manner when the human will perform different subtasks that may require robot assistance. The representation is a graphical model where the start and end of each subtask is explicitly represented as a probabilistic variable conditioned upon prior intervals. This formulation allows the inclusion of uncertain perceptual detections as evidence to drive the predictions. Next, given a cost function that describes the penalty for different wait times, we develop a planning algorithm which selects robot-actions that minimize the expected cost based upon the distribution over predicted human-action timings. We demonstrate the approach in assembly tasks where the robot must provide the right part at the right time depending upon the choices made by the human operator during the assembly.

A representation for structured activities is developed that allows a robot to probabilistically infer which task actions a human is currently performing and to predict which future actions will be executed and when they will occur. The goal is to enable a robot to anticipate collaborative actions in the presence of uncertain sensing and task ambiguity. The system can represent multi-path tasks where the task variations may contain partially ordered actions or even optional actions that may be skipped altogether. The task is represented by an AND-OR tree structure from which a probabilistic graphical model is constructed. Inference methods for that model are derived that support a planning and execution system for the robot which attempts to minimize a cost function based upon expected human idle time. We demonstrate the theory in both simulation and actual human-robot performance of a two-waybranch assembly task. In particular we show that the inference model can robustly anticipate the actions of the human even in the presence of unreliable or noisy detections because of its integration of all its sensing information along with knowledge of task structure.

Given a collection of bags where each bag is a set of images, our goal is to select one image from each bag such that the selected images are from the same object class. We model the selection as an energy minimization problem with unary and pairwise potential functions. Inspired by recent few-shot learning algorithms, we propose an approach to learn the potential functions directly from the data. Furthermore, we propose a fast greedy inference algorithm for energy minimization. We evaluate our approach on few-shot common object recognition as well as object co-localization tasks. Our experiments show that learning the pairwise and unary terms greatly improves the performance of the model over several well-known methods for these tasks. The proposed greedy optimization algorithm achieves performance comparable to state-of-the-art structured inference algorithms while being ∼10 times faster.

Driving is a social activity: drivers often indicate their intent to change lanes via motion cues. We consider mixedautonomy traffic where a Human-driven Vehicle (HV) and an Autonomous Vehicle (AV) drive together. We propose a planning framework where the degree to which the AV considers the other agent's reward is controlled by a selfishness factor. We test our approach on a simulated two-lane highway where the AV and HV merge into each other's lanes. In a user study with 21 subjects and 6 different selfishness factors, we found that our planning approach was sound and that both agents had less merging times when a factor that balances the rewards for the two agents was chosen. Our results on double lane merging suggest it to be a non-zero-sum game and encourage further investigation on collaborative decision making algorithms for mixed-autonomy traffic.

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