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Learning Assistance By Demonstration SystemIt’s been a while since my last post. Excuse: thesis write-up. Update: Thesis submitted!

In other news, our recent work on Learning Assistance by Demonstration was accepted this year’s IROS! It’ll be a fun and interesting conference in Tokyo, Japan! You can find a preprint here.

Abstract:  Crafting a proper assistance policy is a difficult endeavour but essential for the development of robotic assistants. Indeed, assistance is a complex issue that depends not only on the task-at-hand, but also on the state of the user, environment and competing objectives. As a way forward, this paper proposes learning the task of assistance through observation; an approach we term Learning Assistance by Demonstration (LAD). Our methodology is a subclass of Learning-by-Demonstration (LbD), yet directly addresses difficult issues associated with proper assistance such as when and how to appropriately assist. To learn assistive policies, we develop a probabilistic model that explicitly captures these elements and provide efficient, online, training methods. Experimental results on smart mobility assistance — using both simulation and a real-world smart wheelchair platform — demonstrate the effectiveness of our approach; the LAD model quickly learns when to assist  (achieving an AUC score of 0.95 after only one demonstration) and improves with additional examples. Results show that this translates into better task-performance; our LAD-enabled smart wheelchair improved participant driving performance (measured in lap seconds) by 20.6s (a speedup of 137%), after a single teacher demonstration.

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Just got news that our paper on the ARTY smart paediatric wheelchair was accepted to the IROS 2012 Workshop on Progress, Challenges and Future Perspectives in Navigation and Manipulation Assistance for Robotic Wheelchairs.

Abstract: Standard powered wheelchairs are still heavily dependent on the cognitive capabilities of users. Unfortunately, this excludes disabled users who lack the required problem-solving and spatial skills, particularly young children. For these children to be denied powered mobility is a crucial set-back; exploration is important for their cognitive, emotional and psychosocial development. In this paper, we present a safer paediatric wheelchair: the Assistive Robot Transport for Youngsters (ARTY). The fundamental goal of this research is to provide a key-enabling technology to young children who would otherwise be unable to navigate independently in their environment. In addition to the technical details of our smart wheelchair, we present user-trials with able-bodied individuals as well as one 5-year-old child with special needs. ARTY promises to provide young children with “early access” to the path towards mobility independence.

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More information about ARTY (with video).

Just submitted an IROS camera-ready copy of some recent work on online spatio-temporal learning:

In this work, we are primarily concerned with robotic systems that learn online and continuously from multi-variate data-streams. Our first contribution is a new recursive kernel, which we have integrated into a sparse Gaussian Process to yield the Spatio-Temporal Online Recursive Kernel Gaussian Process (STORK-GP). This algorithm iteratively learns from time-series, providing both predictions and uncertainty estimates. Experiments on benchmarks demonstrate that our method achieves high accuracies relative to state-of-the-art methods. Second, we contribute an online tactile classifier which uses an array of STORK-GP experts. In contrast to existing work, our classifier is capable of learning new objects as they are presented, improving itself over time. We show that our approach yields results comparable to highly-optimised offline classification methods. Moreover, we conducted experiments with human subjects in a similar online setting with true-label feedback and present the insights gained.

This work was nominated as a finalist for the 2012 CoTeSys Cognitive Robotics Best Paper Award.

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Paper accepted at IJCNN 2012; Never been to Brisbane!

Summary: In this work, we contribute the online echo state gaussian process (OESGP), a novel Bayesian-based online method that is capable of iteratively learning complex temporal dynamics and producing predictive distributions (instead of point predictions). Our method can be seen as a combination of the echo state network with a sparse approximation of Gaussian processes (GPs). Extensive experiments on the one-step prediction task on well-known benchmark problems show that OESGP produced statistically superior results to current online ESNs and state-of-the-art regression methods. In addition, we characterise the benefits (and drawbacks) associated with the considered online methods, specifically with regards to the trade-off between computational cost and accuracy. For a high-dimensional action recognition task, we demonstrate that OESGP produces high accuracies comparable to a recently published graphical model, while being fast enough for real-time interactive scenarios.

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Independent mobility is crucial for a growing child and its loss can severely impact cognitive, emotional and social development. Unfortunately, powered wheelchair provision for young children has been difficult due to safety concerns. But powered mobility need not be unsafe. Risks can be reduced through the use of robotic technology (e.g., obstacle avoidance) and we present a prototype safe smart paediatric wheelchair: the Assistive Robot Transport for Youngsters (ARTY). A core aspect of our work is that we aim to bring ARTY to the field and we discuss the challenges faced when trying to involve children in the development/testing of medical technology. We discuss one preliminary experiment designed as a “Hide-and-Seek” game as a short case study.

Note: This paper was submitted to the ACM/IEEE HRI-2011 Pioneers Workshop, Lausanne 2011.

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Medical decisions are often difficult; they involve uncertain information, multiple-objectives and debatable outcomes. In this work, we discuss the application of the multi-reward partially-observable Markov decision process (MR-POMDP) and NSGA2-LS, a hybridised multi-objective evolutionary solver, to two problems in the medical domain: anthrax re- sponse and smart-wheelchair control. For the first problem, we use a discrete model and analyse the trade-offs between the best solutions (in the form of finite-state controllers) found by our evolutionary algorithm. For the second, we contribute an extension of our method to the continuous space and optimising recurrent neural networks (RNNs) for use on medical robots such as smart wheelchairs.

Download | ACM Digital Library Link

The structure and properties of public transportation networks have great implications for urban planning, public policies and infectious disease control. We contribute a complex weighted network analysis of travel routes on the Singapore rail and bus transportation systems. We study the two networks using both topological and dynamical analyses. Our results provide additional evidence that a dynamical study adds to the information gained by traditional topological analysis, providing a richer view of complex weighted networks. For example, while initial topological measures showed that the rail network is almost fully connected, dynamical measures highlighted hub nodes that experience disproportionately large traffic. The dynamical assortativity of the bus networks also differed from its topological counterpart. In addition, inspection of the weighted eigenvector centralities highlighted a significant difference in traffic flows for both networks during weekdays and weekends, suggesting the importance of adding a temporal perspective missing from many previous studies.

Download | Physica A Link

Plans and decisions in many real-world scenarios are made under uncertainty and to satisfy multiple, possibly conflicting, objectives. In this work, we contribute the multi-reward partially-observable Markov decision process (MR-POMDP) as a general modelling framework. To solve MR-POMDPs, we present two hybrid (memetic) multi-objective evolutionary algorithms that generate non-dominated sets of policies (in the form of stochastic finite state controllers). Performance comparisons between the methods on multi-objective problems in robotics (with 2, 3 and 5 objectives), web-advertising (with 3, 4 and 5 objectives) and infectious disease control (with 3 objectives), revealed that memetic variants outperformed their original counterparts. We anticipate that the MR-POMDP along with multi-objective evolutionary solvers will prove useful in a variety of theoretical and real-world applications.

Download | ACM Digital Library Link

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