Compliant motions allow alignment of workpieces using naturally occurring interaction forces. However, free-floating objects do not have a fixed base to absorb the reaction forces caused by the interactions. Consequently, if the interaction forces are too high, objects can gain momentum and move away after contact. This paper proposes an approach based on direct force control for compliant manipulation of free-floating objects. The objective of the controller is to minimize the interaction forces while maintaining the contact. The proposed approach achieves this by maintaining small constant force along the motion direction and an apparent reduction of manipulator inertia along remaining Degrees of Freedom (DOF). Simulation results emphasize the importance of relative inertia of the robotic manipulator with respect to the free-floating object. The experiments were performed with KUKA LWR4+ manipulator arm and a two-dimensional micro-gravity emulator (object floating on an air bed), which was developed in-house. It was verified that the proposed control law is capable of controlling the interaction forces and aligning the tools without pushing the object away. We conclude that direct force control works better with a free-floating object than implicit force control algorithms, such as impedance control.

Overlaying differential changes in gene expression on protein interaction networks has proven to be a useful approach to interpreting the cell's dynamic response to a changing environment. Despite successes in finding active subnetworks in the context of a single species, the idea of overlaying lists of differentially expressed genes on networks has not yet been extended to support the analysis of multiple species' interaction networks. To address this problem, we designed a scalable, cross-species network search algorithm, neXus (Network-cross(X)-species-Search), that discovers conserved, active subnetworks based on parallel differential expression studies in multiple species. Our approach leverages functional linkage networks, which provide more comprehensive coverage of functional relationships than physical interaction networks by combining heterogeneous types of genomic data. We applied our cross-species approach to identify conserved modules that are differentially active in stem cells relative to differentiated cells based on parallel gene expression studies and functional linkage networks from mouse and human. We find hundreds of conserved active subnetworks enriched for stem cell-associated functions such as cell cycle, DNA repair, and chromatin modification processes. Using a variation of this approach, we also find a number of species-specific networks, which likely reflect mechanisms of stem cell function that have diverged between mouse and human. We assess the statistical significance of the subnetworks by comparing them with subnetworks discovered on random permutations of the differential expression data. We also describe several case examples that illustrate the utility of comparative analysis of active subnetworks.