  Intricate behaviors an organism can exhibit is predicated on its ability to
sense and effectively interpret complexities of its surroundings. Relevant
information is often distributed between multiple modalities, and requires the
organism to exhibit information assimilation capabilities in addition to
information seeking behaviors. While biological beings leverage multiple
sensing modalities for decision making, current robots are overly reliant on
visual inputs. In this work, we want to augment our robots with the ability to
leverage the (relatively under-explored) modality of touch. To focus our
investigation, we study the problem of scene reconstruction where touch is the
only available sensing modality. We present Tactile Slam (tSLAM) -- which
prepares an agent to acquire information seeking behavior and use implicit
understanding of common household items to reconstruct the geometric details of
the object under exploration. Using the anthropomorphic `ADROIT' hand, we
demonstrate that tSLAM is highly effective in reconstructing objects of varying
complexities within 6 seconds of interactions. We also established the
generality of tSLAM by training only on 3D Warehouse objects and testing on
ContactDB objects.
