Conflicts of Jurisdiction Under the New Restatement

In many scenarios, domestic robot will regularly encounter unknown objects. In such cases, top-down knowledge about the object for detection, recognition, and classification cannot be used. To learn about the object, or to be able to grasp it, bottom-up object segmentation is an important competence for the robot. Also when there is top-down knowledge, prior segmentation of the object can improve recognition and classification. In this paper, we focus on the problem of bottom-up detection and segmentation of unknown objects. Gestalt psychology studies the same phenomenon in human vision. We propose the utilization of a number of Gestalt principles. Our method starts by generating a set of hypotheses about the location of objects using symmetry. These hypotheses are then used to initialize the segmentation process. The main focus of the paper is on the evaluation of the resulting object segments using Gestalt principles to select segments with high figural goodness. The results show that the Gestalt principles can be successfully used for detection and segmentation of unknown objects. The results furthermore indicate that the Gestalt measures for the goodness of a segment correspond well with the objective quality of the segment. We exploit this to improve the overall segmentation performance.© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. QC 20111115EU project eSMCs, IST-FP7-IP-270212SSF RoS

VPE: Variational Policy Embedding for Transfer Reinforcement Learning

Reinforcement Learning methods are capable of solving complex problems, but resulting policies might perform poorly in environments that are even slightly different. In robotics especially, training and deployment conditions often vary and data collection is expensive, making retraining undesirable. Simulation training allows for feasible training times, but on the other hand suffers from a reality-gap when applied in real-world settings. This raises the need of efficient adaptation of policies acting in new environments. We consider this as a problem of transferring knowledge within a family of similar Markov decision processes. For this purpose we assume that Q-functions are generated by some low-dimensional latent variable. Given such a Q-function, we can find a master policy that can adapt given different values of this latent variable. Our method learns both the generative mapping and an approximate posterior of the latent variables, enabling identification of policies for new tasks by searching only in the latent space, rather than the space of all policies. The low-dimensional space, and master policy found by our method enables policies to quickly adapt to new environments. We demonstrate the method on both a pendulum swing-up task in simulation, and for simulation-to-real transfer on a pushing task