No abstract

In this paper we present a new approach for semantic recognition in the context of robotics. When a robot evolves in its environment, it gets 3D information given either by its sensors or by its own motion through 3D reconstruction. Our approach uses (i) 3D-coherent synthesis of scene observations and (ii) mix them in a multi-view framework for 3D labeling. (iii) This is efficient locally (for 2D semantic segmentation) and globally (for 3D structure labeling). This allows to add semantics to the observed scene that goes beyond simple image classification, as shown on challenging datasets such as SUNRGBD or the 3DRMS Reconstruction Challenge. * Computed at low resolution (224x224) as in [23] on the contrary of all other results computed at native resolution. **We also test a High Definition strategy, cropping 224x244 patches at original resolution instead of warping image.

SUMMARY In human–robot comanipulation, virtual guides are an important tool used to assist the human worker as they constrain the movement of the robot to improve the task accuracy and to avoid undesirable effects, such as collisions with the environment. Consequently, the physical effort and cognitive overload are reduced during accomplishment of comanipulative tasks. However, the construction of virtual guides often requires expert knowledge and modeling of the task, which restricts the usefulness of virtual guides to scenarios with fixed constraints. Moreover, few approaches have addressed the implementation of virtual guides enforcing orientation constraints and, when done, these approaches have treated translation and orientation separately, and consequently there is no synchronization of the translational and rotational motions. To overcome these challenges and enhance the programming flexibility of virtual guides, we present a new framework that allows the user to create 6D virtual guides through XSplines which we define as a combination of Akima splines for the translation component and spherical cubic interpolation of quaternions for the orientation component. For complex tasks, the user is able to initially define a 3D virtual guide and then use this assistance in translational motion to concentrate only on defining the orientations along the path. It is also possible for the user to modify a particular point or portion of a guide while being assisted by it. We demonstrate in an industrial scenario that these innovations provide an intuitive solution to extend the use of virtual guides to 6 degrees of freedom and increase the human worker’s comfort during the programming phase of these guides in an assisted human–robot comanipulation context.

No abstract