Sequence Similarity for Hidden State Estimation

Little work has been done on comparing long- and short-term memory (LTM and STM) traces in the context of hidden state estimation in POMDPs.  Belief-state algorithms use a probabilistic step-by-step approach which should be optimal, but doesn't scale well and has an unrealistic requirement for knowledge of the state space underlying observations, 

The instance-based Nearest Sequence Memory (NSM) algorithm performs remarkably well without any knowledge of the underlying state space.  Instead is compares previously observed sequences of observations and actions in LTM with a recently observed sequence in STM to estimate the underlying state.  The NSM algorithm uses a count of matching observation-action records as a metric for sequence proximity.

In problems where certain observation-actions are particularly salient, e.g., passing through a 'door' in Sutton's world, or picking up a passenger in the Taxi problem, a simple match count is not a particularly good sequence proximity metric and, as a result, I have recently been casting around for other work on such metrics.

ADD SLTM work reference here.

I have come across some interesting work on sequence comparison by Z. Meral Ozsoyoglu, Cenk Sahinalp and Piotr Indyk on Improving Proximity Search for Sequences.  This work has been done in the context of genome sequencing could be an interesting starting point for going beyond the simple match count metric used by NSM.

Robagogy - Imitating and exploring

Just saw this new article on how human children mix imitation and exploration entitled Imitation and Innovation: The Dual Engines of Cultural Learning.  It seems that the level to which they rely on imitation is reduced reliably with increased performance.  This should inform our work on robot learning.
This and similar insights from psychology makes me think we should define a new field of robagogy, meaning 'how to teach robots'.  This area should be informed by pedagogy 'how to teach children' and the less popular andragogy or 'how to teach adults'.  

Beyond Random Motor Babbling: How to explore when learning behaviors

Humans can learn or improve skills by practicing, e.g., tennis strokes improve as you spend time whacking balls against a wall.  This process is likely to involve exploration of a problem space, in the case of tennis, trying different the actuator values to develop appropriate responses to a range of different ball trajectories and speeds.

In robot skill learning, an important question is 'how do we do this exploration?'  Many papers use random motor babbling [refs], typically within a safe envelope of values and most commonly with a flat probability distribution within each range [refs].  It is highly unlikely that this is how humans and animals explore.

It has been recognised for some time [ref] that system noise can provide sufficient variability to support exploration leading to the identification of effective solutions.  Pinheiro et al. [1] just showed that such variability is also a sufficient requirement for learning new skills in humans.
Think more about using this to inspire new, biologically more plausible, exploration strategies for robot learning.

[1] João de Paula Pinheiro, Pricila Garcia Marques, Go Tani and Umberto Cesar Corrêa (2015) Diversification of motor skills rely upon an optimal amount of variability of perceptive and motor task demands.  In Adaptive Behavior, (only online so far http://adb.sagepub.com/content/early/2015/02/23/1059712315571369?papetoc ).

Other health care projects

There are a number of healthcare research projects that are highly relevant to tele-care.  Below are the ones I know about so far:

•  Sensor Platform for HEalthcare in a Residential Environment (SPHERE), EPSRC

Tele-assisted living

The idea behind tele-assisted living is that many services can be provided remotely if there is a mobile manipulator connected to the Internet in someone's home.
Tele-operating this platform will remove the requirement for high levels of robot autonomy which we are not likely to see for decades.

Such a platform could also provide a large amount of training data to speed up the development of robot autonomy.  My interest is in the area of robot learning and the delegation of skills from a tele-operator to the robot.

Many research activities are currently ongoing in this area and this blog post is meant as a list of these.

FP7 projects

• ACCOMPANY: Acceptable robotiCs COMPanions for AgeiNg Years (includes Hertfordshire and Birmingham)
• AALIANCE2: The European Ambient Assisted Living Innovation Alliance (includes Tunstall Healthcare Ltd., UK)
• CAPSIL: International Support of a Common Awareness and Knowledge Platform for Studying and Enabling Independent Living (FP7 Support Action, includes Imperial College, London)
• CONFIDENCE: Ubiquitous Care System to Support Independent Living (no UK partner)
• DOMEO: Domestic robot for elderly assisteance (no UK partner) 
• FLORENCE: Multi-Purpose Mobile Robot for Ambient Assisted Living (no UK partners)
• KSERA: Knowledgeable Service Robots for Ageing (no UK partners)
• MOBISERV: An Integrated Intelligent Home Environment for the Provision of Health, Nutrition and Mobility Services to the Elderly (includes Bristol)
• ROBOT-ERA : Implementation and Integration of Advanced Robotic Systems and Intelligent Environments in Real Scenarios for the Ageing Population (includes Plymouth)
• SCRIPT: Supervised Care and Rehabilitation Involving Personal Tele-Robotics (includes Hertfordshire and Sheffield)
• SRS: Multi-Role Shadow Robotic System for Independent Living (includes Cardiff and Bedfordshire)

Interest groups

• KT-EQUAL (Sheffield)

Horizontal Connections

I have still to find a neural model of the competitive selection mechanism that finds the winning node of a SOM. The process has been parallelised many times for parallel SOM implementations based on FPGAs or CUDA (such as Aquila).  There is a lot of work on horizontal/lateral connections in the cortex.  It would be very interesting to study a SOM implementation using such lateral connections to select SOM winners.

Endogenously Active Elements

Endogenous activity has been raised as an important mechanism for cognition, both in terms of neural self-organisation (Choa and Choi 2012) and for control (Bechtel and Abrahamsen 2010 ).  Such a mechanism would be a very interesting extension to our PLANCS framework for behavior-based robotics (Dahl and Giraud-Carrier 2005).

• Bechtel, W. and Abrahamsen, A. (2010) Understanding the brain as an endogenously active mechanism. In Proceedings of the 32nd Annual Conference of the Cognitive Science Society, Austin, Texas. (see Bechtel's web site http://mechanism.ucsd.edu/~bill/index.html)
• Choa, M. W. and Choi, M. Y. (2012) Spontaneous organization of the cortical structure through endogenous neural firing and gap junction transmission, Neural Networks 31:46–52.
• T. S. Dahl and C. Giraud-Carrier, "Incremental Development of Adaptive Behaviors using Trees of Self-Contained Solutions," Adaptive Behavior, 13(3)243-260, 2005.