A Unifying Variational Framework for Gaussian Process Motion Planning

To control how a robot moves, motion planning algorithms must compute paths in high-dimensional state spaces while accounting for physical constraints related to motors and joints, generating smooth and stable motions, avoiding obstacles, and preventing collisions. A motion planning algorithm must therefore balance competing demands, and should ideally incorporate uncertainty to handle noise, model errors, and facilitate deployment in complex environments. To address these issues, we introduce a framework for robot motion planning based on variational Gaussian Processes, which unifies and generalizes various probabilistic-inference-based motion planning algorithms. Our framework provides a principled and flexible way to incorporate equality-based, inequality-based, and soft motion-planning constraints during end-to-end training, is straightforward to implement, and provides both interval-based and Monte-Carlo-based uncertainty estimates. We conduct experiments using different environments and robots, comparing against baseline approaches based on the feasibility of the planned paths, and obstacle avoidance quality. Results show that our proposed approach yields a good balance between success rates and path quality

Measuring effectiveness, efficiency and equity in a Payments for Ecosystem Services trial.

There is currently a considerable effort to evaluate the performance of payments for ecosystem services (PES) as an environmental management tool. The research presented here contributes to this work by using an experimental design to evaluate PES as a tool for supporting biodiversity conservation in the context of an African protected area. The trial employed a 'before and after' and 'with and without' design. We present the results of social and ecological surveys to investigate the impacts of the PES in terms of its effectiveness, efficiency and equity. We find the PES to be effective at bringing about additional conservation outcomes. However, we also found that increased monitoring is similarly effective in the short term, at lower cost. The major difference - and arguably the significant contribution of the PES - was that it changed the motives for protecting the park and improved local perceptions both of the park and its authority. We discuss the implications of these results for conservation efficiency, arguing that efficiency should not be defined in terms of short-term cost-effectiveness, but also in terms of the sustainability of behavioral motives. This insight helps us to resolve the apparent trade-off between goals of equity and efficiency in PES

Response of sensitive behaviors to frequent measurement

We study the influence of frequent survey measurement on behavior. Widespread access to the Internet has made important breakthroughs in frequent measurement possible—potentially revolutionizing social science measurement of processes that change quickly over time. One key concern about using such frequent measurement is that it may influence the behavior being studied. We investigate this possibility using both a population-based experiment with random assignment to participation in a weekly journal for twelve months (versus no journal) and a large scale population-based journal-keeping study with weekly measurement for 30 months. Results reveal few of the measured behaviors are correlated with assignment to frequent measurement. Theoretical reasoning regarding the likely behavioral response to frequent measurement correctly predicts domains most vulnerable to this possibility. Overall, however, we found little evidence of behavioral response to frequent measurement