Robot navigation in dense crowds : statistical models and experimental studies of human robot cooperation

This thesis explores the problem of mobile robot navigation in dense human crowds. We begin by considering a fundamental impediment to classical motion planning algorithms called the freezing robot problem: once the environment surpasses a certain level of complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place (or performs unnecessary maneuvers) to avoid collisions. Since a feasible path typically exists, this behavior is suboptimal. Existing approaches have focused on reducing predictive uncertainty by employing higher fidelity individual dynamics models or heuristically limiting the individual predictive covariance to prevent overcautious navigation. We demonstrate that both the individual prediction and the individual predictive uncertainty have little to do with this undesirable navigation behavior. Additionally, we provide evidence that dynamic agents are able to navigate in dense crowds by engaging in joint collision avoidance, cooperatively making room to create feasible trajectories. We accordingly develop interacting Gaussian processes, a prediction density that captures cooperative collision avoidance, and a "multiple goal" extension that models the goal driven nature of human decision making. Navigation naturally emerges as a statistic of this distribution.

Most importantly, we empirically validate our models in the Chandler dining hall at Caltech during peak hours, and in the process, carry out the first extensive quantitative study of robot navigation in dense human crowds (collecting data on 488 runs). The multiple goal interacting Gaussian processes algorithm performs comparably with human teleoperators in crowd densities nearing 1 person/m², while a state of the art noncooperative planner exhibits unsafe behavior more than 3 times as often as the multiple goal extension, and twice as often as the basic interacting Gaussian process approach. Furthermore, a reactive planner based on the widely used dynamic window approach proves insufficient for crowd densities above 0.55 people/m². We also show that our noncooperative planner or our reactive planner capture the salient characteristics of nearly any dynamic navigation algorithm. For inclusive validation purposes, we show that either our non-interacting planner or our reactive planner captures the salient characteristics of nearly any existing dynamic navigation algorithm. Based on these experimental results and theoretical observations, we conclude that a cooperation model is critical for safe and efficient robot navigation in dense human crowds.

Finally, we produce a large database of ground truth pedestrian crowd data. We make this ground truth database publicly available for further scientific study of crowd prediction models, learning from demonstration algorithms, and human robot interaction models in general.

Real-World Social Cognition: Context Effects in Face and Threat Processing

As borne out by everyday social experience, social cognition is highly dependent on context, modulated by a host of factors that arise from the social environment in which we live. While streamlined laboratory research provides excellent experimental control, it can be limited to telling us about the capabilities of the brain under artificial conditions, rather than elucidating the processes that come into play in the real world. Consideration of the impact of ecologically valid contextual cues on social cognition will improve the generalizability of social neuroscience findings also to pathology, e.g., to psychiatric illnesses. To help bridge between laboratory research and social cognition as we experience it in the real world, this thesis investigates three themes: (1) increasing the naturalness of stimuli with richer contextual cues, (2) the potentially special contextual case of social cognition when two people interact directly, and (3) a third theme of experimental believability, which runs in parallel to the first two themes. Focusing on the first two themes, in work with two patient populations, we explore neural contributions to two topics in social cognition. First, we document a basic approach bias in rare patients with bilateral lesions of the amygdala. This finding is then related to the contextual factor of ambiguity, and further investigated together with other contextual cues in a sample of healthy individuals tested over the internet, finally yielding a hierarchical decision tree for social threat evaluation. Second, we demonstrate that neural processing of eye gaze in brain structures related to face, gaze, and social processing is differently modulated by the direct presence of another live person. This question is investigated using fMRI in people with autism and controls. Across a range of topics, we demonstrate that two themes of ecological validity — integration of naturalistic contextual cues, and social interaction — influence social cognition, that particular brain structures mediate this processing, and that it will be crucial to study interaction in order to understand disorders of social interaction such as autism.

Three essays on economics and information shocks

A person living in an industrialized society has almost no choice but to receive information daily with negative implications for himself or others. His attention will often be drawn to the ups and downs of economic indicators or the alleged misdeeds of leaders and organizations. Reacting to new information is central to economics, but economics typically ignores the affective aspect of the response, for example, of stress or anger. These essays present the results of considering how the affective aspect of the response can influence economic outcomes.

The first chapter presents an experiment in which individuals were presented with information about various non-profit organizations and allowed to take actions that rewarded or punished those organizations. When social interaction was introduced into this environment an asymmetry between rewarding and punishing appeared. The net effects of punishment became greater and more variable, whereas the effects of reward were unchanged. The individuals were more strongly influenced by negative social information and used that information to target unpopular organizations. These behaviors contributed to an increase in inequality among the outcomes of the organizations.

The second and third chapters present empirical studies of reactions to negative information about local economic conditions. Economic factors are among the most prevalent stressors, and stress is known to have numerous negative effects on health. These chapters document localized, transient effects of the announcement of information about large-scale job losses. News of mass layoffs and shut downs of large military bases are found to decrease birth weights and gestational ages among babies born in the affected regions. The effect magnitudes are close to those estimated in similar studies of disasters.