Scene Identification based onConcept LearningBeatrice van Eden

- Part time PhD Student at the University of the Witwatersrand.- Fulltime employee of the Council for Scientific and Industrial Research.

Index• Broad Problem Statement Give an overview of the researchI am busy with. • Steering Towards a SolutionOutline the abstract hierarchy that I consider to bring me closer to the Solution.

• Related workHighlights of some of the reading I did.

iCub baby robot as used in JS' EU project IM-CLEVER on developmental robotics and on implementing the theory of creativity & curiosity & novelty & interestingness on robots.

Broad Problem Statement

• How can we give a mobile robot the capability to continuously and autonomously form concepts of its environment?• Humans can identify a kitchen area by recalling the concept formed though previous

experience.• Certain actions and visible items contributes the concept of a environment being formed and

remembered.• Machines/robots can exhibit a range of autonomous behaviour but autonomous concept

forming is researched in this study.

Steering Towards a Solution

Image of scene

Identify items in scene

Determine most feasible scene

Recognise the scene / contribute

to a concept

Related Work - Papers

Searching for objects driven by context(Bogdan Alexe and Heess, Nicolas and

Yee W. Teh and Vittorio Ferrari)

Scemantic Mapping Using Object-Class Segmentation of RGB-D Images(Stuckler, J.; Biresev, N.; Behnke, S)

Unsupervised feature learning for 3D scene labeling(Lai, K.; Liefeng Bo; Fox, D)

3-Sweep: extracting editable objects from a single photo(Tao Chen, Zhe Zhu, Ariel Shamir, Shi-Min

Hu, Daniel Cohen)

Related Work - Highlights • How to make robots learn from experience?• Traditional reinforcement learning (Schmidhuber, CoTeSys)• Limited to simple reactive behaviour • Not work well for realistic robots• Why? Dimensional explosion, continuous spaces

• Robot learning in realistic environments• Novel algorithms

• Learning to identify important events in sensory stream• Temporarily memorize them in adaptive, dynamic, internal states

• Most promising approaches:• Recurrent neural networks, policy gradients, and the optimal

ordered problem solver.

Terms• Recurrent neural networks - is a class of artificial neural network

where connections between units form a directed cycle. • Policy gradients - are a type of reinforcement learning techniques

that rely upon optimizing parametrized policies with respect to the expected return (long-term cumulative reward) by gradient descent. They do not suffer from many of the problems that have been marring traditional reinforcement learning approaches such as the lack of guarantees of a value function, the intractability problem resulting from uncertain state information and the complexity arising from continuous states & actions

• Optimal Ordered Problem Solver - a general and in a certain sense time-optimal way of solving one problem after another, efficiently searching the space of programs that compute solution candidates, including those programs that organize and manage and adapt and reuse earlier acquired knowledge.

Thank you