Student Projects propositions for 2014 Autumn
If you are interested in one of the below projects, Semester Projects or Master Projects, please contact the first person of reference indicated in each description either by telephone, or by email, or by visiting us directly to the LASA offices. If you are looking for a project for 2015 Spring, please click here.
Reaching for a moving object with YuMi (this project is assigned)The use of multiarm robotic systems allows for highly complex manipulation of heavy objects that would otherwise be impossible for a singlearm robot. In our work [1], we propose a unified coordinated control architecture for reaching and grabbing a moving object by a multiarm robotic system. Due to the complexity of the task and the system, each arm must coordinate not only with the object’s motion but also with the motion of other arms, in both task and joint spaces. At the taskspace level, the proposed unified dynamical system coordinates the motion of each arm with the rest of the arms and the resultant motion of the arms with that of the object. At the joint space level, the coordination between the arms is achieved by introducing a centralized inverse kinematics (IK) solver under datadriven selfcollision avoidance constraints; formulated as a quadratic programming problem (QP) and solved in realtime.


Learning Manipulation with 4 Robotic Arms (this project is assigned)Many industrial tasks require to have several robotic arms working on the same piece simultaneously. This is very difficult as we want the robots to perform the task while not intercepting each other. The joint workspace of the robots is highly nonconvex and cannot be expressed mathematically. This project will apply machine learning techniques to learn a representation of the feasible workspace of the 4 robotic arms. This representation will then be used in an inverse kinematic controller to control for the robot's motions at run time. The algorithm will be validated to control 4 robotic arms in the lab that must manipulate objects on a moving conveyer belt.


Reaching for a moving object with YuMiThe use of multiarm robotic systems allows for highly complex manipulation of heavy objects that would otherwise be impossible for a singlearm robot. In our work [1], we propose a unified coordinated control architecture for reaching and grabbing a moving object by a multiarm robotic system. Due to the complexity of the task and the system, each arm must coordinate not only with the object’s motion but also with the motion of other arms, in both task and joint spaces. At the taskspace level, the proposed unified dynamical system coordinates the motion of each arm with the rest of the arms and the resultant motion of the arms with that of the object. At the joint space level, the coordination between the arms is achieved by introducing a centralized inverse kinematics (IK) solver under datadriven selfcollision avoidance constraints; formulated as a quadratic programming problem (QP) and solved in realtime.


Sparse Solutions for LargeScale Regression Problems
The curse of dimensionality is one of the main challenges in 'Big Data' problems. Unless the learning algorithm has an explicitly imposed sparsity constraint, model complexity will undoubtedly increase with respect to the number of samples. Typical sparse solutions for regression focus on problems where the number of samples "M" is less than the input "P", i.e. "M P", specifically, datasets with > 100,000 samples and where a sparse solution is needed for efficient prediction. Two kernelbased methods exist that are formulated to tackle such problems: 1) Relevance Vector Machines, a Bayesian formulation of Support Vector Machines that applies the Bayesian ‘Automatic Relevance Determination’ (ARD) methodology to linear kernel models. 2) Sparse Gaussian Process with PseudoInputs, whose covariance is parameterized by the locations of "M" pseudoinput points, which we learn by a gradient based optimization (analogous to 'relevant vectors'). Nevertheless, both of these algorithms do not scale to data >100k training samples due to their optimization during training. Based on the literature of 1) and 2), the student should extend one of these algorithms to be capable of handling larger datasets, either by a) reformulating the optimization problem, such that it becomes feasible or b) tackle it with a divideandconquer approach and partition the large dataset into smaller subsets where 1) or 2) can be learned and merging/appropriate aggregation schemes must be introduced. The proposed approach will then be validated on interesting realworld dataset with M > 100k. The solution shall be implemented in Matlab/Python/C++ (the students choice).
[1] Michael Tipping. Relevance vector machine, October 14 2003. US Patent
6,633,857


Towards Incremental Learning: Merging SVMs from independent sample sets
With the increase in data available online and everchanging applications, incremental and online machine learning algorithms that can adapt, learn and unlearn will become essential in the near future. Support Vector Machines (SVM) are undoubtedly one of the most powerful machine learning algorithms to date, however, due to the nature of the posed optimization problem (batch learning), they fall short when applied to incremental/adaptive problems. In this work, we are interested in finding a suitable solution for the problem of "incomplete datasets" or "complementary datasets" for a classification problem. Assume we are given a dataset at a specific point in time and we must learn a model to start predicting immediately. Then, we are suddenly given a new set of samples which belong to the same dataset. The question now is: What do we do with this new data? Do we relearn the entire model with all the datapoints? What if the samples are contradictory? Can we learn a new decision function from the new samples and merge them to the old model, without hindering performance on classification? Can we incrementally update the old model with our new samples? What if we suddenly realize that some samples were labeled erroneously and we would like to 'unlearn' them? These are the [subset of] questions that the student should try to answer. Seldom work in SVM literature is capable of handling these issues. The few works that can, are categorized into 1) "active/online methods" where training points are fed onebyone and the SVM is learned sequentially [1] and 2) "ensemble methods" where a dataset is 'partitioned' into Nsets where NSVMs are learned and basic aggregation schemes are applied to generate a final machine [2]. These approaches, however, are mostly suitable for handling large datasets and focus primarily on improving training time (i.e. efficient learning). By leveraging ideas from 1), 2) and online convex optimization [3], the student must propose an efficient and adaptable SVM learning scheme capable of solving all [or a subset] of the issues imposed by the proposed incremental learning problem. The solution shall be implemented in Matlab/Python/C++ (the students choice).
[1] Antoine Bordes, Seyda Ertekin, Jason Weston and Léon Bottou: Fast Kernel Classifiers with Online and Active Learning, Journal of Machine Learning Research, 6:15791619, September 2005.


Robot teleoperation Combining muscular activity with gazeAn important part of neuroprosthetic control is to decode user’s motion intention. This intention is then converted into appropriate movements for the prosthetic or assistive device. When controlling prosthetic handarm systems, one can use eye movements as a natural way to determine the object the user intends to grasp. Eye movements give only the direction in which the object of interest may be located but not the exact location.


Learning Manipulation with 4 Robotic ArmsMany industrial tasks require to have several robotic arms working on the same piece simultaneously. This is difficult as the robot should not intercept each other while performing the task. The joint workspace of the robot is highly nonconvex and cannot be expressed mathematically. This project will apply machine learning techniques to learn a representation of the feasible workspaces f 4 robotic arms. This representation will then be used in an inverse kinematic controller to control for the robot's motions at run time. The algorithm will be validated to control 4 robotic arm in the lab that must manipulate objects on a moving conveyer belt. It will also extend the approach to enable to manipulate the object under perurbations, such as when the conveyer belt slows down or accelerates rapidly.

