Learning symbolic representations of actions from human demonstrations

In this paper, a robot learning approach is pro- posed which integrates Visuospatial Skill Learning, Imitation Learning, and conventional planning methods. In our approach, the sensorimotor skills (i.e., actions) are learned through a learning from demonstration strategy. The sequence of per- formed actions is learned through demonstrations using Visu- ospatial Skill Learning. A standard action-level planner is used to represent a symbolic description of the skill, which allows the system to represent the skill in a discrete, symbolic form. The Visuospatial Skill Learning module identifies the underlying constraints of the task and extracts symbolic predicates (i.e., action preconditions and effects), thereby updating the planner representation while the skills are being learned. Therefore the planner maintains a generalized representation of each skill as a reusable action, which can be planned and performed inde- pendently during the learning phase. Preliminary experimental results on the iCub robot are presented

Neural Task Programming: Learning to Generalize Across Hierarchical Tasks

In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program induction. NTP takes as input a task specification (e.g., video demonstration of a task) and recursively decomposes it into finer sub-task specifications. These specifications are fed to a hierarchical neural program, where bottom-level programs are callable subroutines that interact with the environment. We validate our method in three robot manipulation tasks. NTP achieves strong generalization across sequential tasks that exhibit hierarchal and compositional structures. The experimental results show that NTP learns to generalize well to- wards unseen tasks with increasing lengths, variable topologies, and changing objectives.Comment: ICRA 201

Affordances of spreadsheets in mathematical investigation: Potentialities for learning

This article, is concerned with the ways learning is shaped when mathematics problems are investigated in spreadsheet environments. It considers how the opportunities and constraints the digital media affords influenced the decisions the students made, and the direction of their enquiry pathway. How might the leraning trajectory unfold, and the learning process and mathematical understanding emerge? Will the spreadsheet, as the pedagogical medium, evoke learning in a distinctive manner? The article reports on an aspect of an ongoing study involving students as they engage mathematical investigative tasks through digital media, the spreadsheet in particular. In considers the affordances of this learning environment for primary-aged students

Considering the anchoring problem in robotic intelligent bin picking

Random Bin Picking means the selection by a robot of a particular item from a container (or bin) in which there are many items randomly distributed. Generalist robots and the Anchoring Problem should be considered if we want to provide a more general solution, since users want that it works with different type of items that are not known 'a priori'. Therefore, we are working on an approach in which robot learning and human-robot interaction are used to anchor control primitives and robot skills to objects and action symbols while the robot system is running, but we are limiting the scope to the packaging domain. In this paper we explain how to use our system to do anchoring in Robotic Bin Picking.Peer ReviewedPostprint (author's final draft