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In the Hubble Space Telescope/Cosmic Origins Spectrograph spectrum of the Seyfert 1 galaxy 2MASX J14292507+4518318, we have identified a narrow absorption line outflow system with a velocity of −151 km s−1. This outflow exhibits absorption troughs from the resonance states of ions like C iv, N v, S iv, and Si ii, as well as excited states from C ii* and Si ii*. Our investigation of the outflow involved measuring ionic column densities and conducting photoionization analysis. These allow the total column density of the outflow to be estimated as log NH = 19.84 cm−2, its ionization parameter to be log UH = −2.0, and its electron number density to be log ne = 2.75 cm−3. These measurements enabled us to determine the mass-loss rate and the kinetic luminosity of the outflow system to be $\dot{M}$ = 0.22 $\mathrm{ M}_{\odot } \, \mathrm{ yr}^{-1}$ and $\log \dot{E_{\mathrm{ K}}}$ = 39.3 erg s−1, respectively. We have also measured the location of the outflow system to be at ∼275 pc from the central source. This outflow does not contribute to the active galactic nucleus (AGN) feedback processes due to the low ratio of the outflow’s kinetic luminosity to the AGN’s Eddington luminosity ($\dot{E_{\mathrm{ K}}}/{L_{\mathrm{ Edd}}}\approx 0.00025 {{\, \rm per\, cent}}$). This outflow is remarkably similar to the two bipolar lobe outflows observed in the Milky Way by XMM–Newton and Chandra.

 

To perform manipulation tasks in the real world, robots need to operate on objects with various shapes, sizes and without access to geometric models. To achieve this it is often infeasible to train monolithic neural network policies across such large variations in object properties. Towards this generalization challenge, we propose to learn modular task policies which compose object-centric task-axes controllers. These task-axes controllers are parameterized by properties associated with underlying objects in the scene. We infer these controller parameters directly from visual input using multi- view dense correspondence learning. Our overall approach provides a simple and yet powerful framework for learning manipulation tasks. We empirically evaluate our approach on 3 different manipulation tasks and show its ability to generalize to large variance in object size, shape and geometry. 

Manipulation tasks can often be decomposed into multiple subtasks performed in parallel, e.g., sliding an object to a goal pose while maintaining con- tact with a table. Individual subtasks can be achieved by task-axis controllers defined relative to the objects being manipulated, and a set of object-centric controllers can be combined in an hierarchy. In prior works, such combinations are defined manually or learned from demonstrations. By contrast, we propose using reinforcement learning to dynamically compose hierarchical object-centric controllers for manipulation tasks. Experiments in both simulation and real world show how the proposed approach leads to improved sample efficiency, zero-shot generalization to novel test environments, and simulation-to-reality transfer with- out fine-tuning.