Public Repository

You can access Tapia-Lab's publicly available repositories using Git® at LoboGit.

Exploring Learning for Intercepting Projectiles with a Robot-Held Stick

It is well known that the choice of input features for a learning task can strongly impact the success of learning. Nobody had previously looked into an important input feature choice for robotic interception tasks, however: whether to represent the robot by its joint angle rotations or spatial link positions. In this work we considered, in a deep reinforcement learning context, a task in which a robot arm must use a stick attached to its end effector to intercept a projectile. While joint angles are a standard choice for robot representation, we found that link positions produced far better success in the interception task. We speculate that the neural network is better able to calculate important distances, such as the distance from the stick to the projectile, when given spatial link positions instead of joint angles. Code and trained policies are available on the project's LoboGit page.

John E. G. Baxter, Torin Adamson, Satomi Sugaya and Lydia Tapia, "Exploring Learning for Intercepting Projectiles with a Robot-Held Stick", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021.(pdf,video)

Software License

Model Data
Learned Policies Data

Swept Volume Geometry

John Baxter, Mohammad R. Yousefi, Satomi Sugaya, Marco Morales and Lydia Tapia, "Deep Prediction of Swept Volume Geometries: Robots and Resolutions", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020.(pdf)


RobotNetwork ScriptTraining Data
Closed Loop (CL)ClosedLoop9DOFClosedLoop_18_41x50x3
Kuka10Kuka7DOF_101010 Kuka_14_10x10x10

* There was an issue with data conversion. Currently in progress.

Robotics Research During a Pandemic

The world has been gripped by the COVID-19 pandemic, and the rightful first reaction of many of us has been to protect the safety and health of ourselves and our families. However, life is not completely paused, and we still have responsibilities to our research, classes, and peers. Rapidly implemented workfrom- home protocols have resulted in a widely lamented lack of worklife balance and progress in research. This column tackles these woes by presenting advice collected from the Women in Engineering (WIE) Committee of the IEEE Robotics and Automation Society on their best practices for continuing work during this challenging time. It also shares some perspectives of robotics students on how they have been impacted by the pandemic.

Covid Lab Safety Signs[1]

[1]Elena Delgado and Lydia Tapia, "Robotics Research During a Pandemic", In IEEE Robotics & Automation Magazine (RAM), September 2020.(article)

Molecular Docking

The models for the receptor (white), caffeine (black) and adenosine (purple)

What do flavorings, perfumes, hormones, neurotransmitters, and allergens have in common? They are all triggers of biological processes that start with a small molecule binding to a cell protein receptor. In our lab, we are interested in understanding the basic mechanisms of ligand-receptor binding.

One example of how ligand-receptor binding affects our lives is the blockage of adenosine receptors by caffeine. Adenosine-receptor binding causes sleepiness. But when caffeine binds to the adenosine receptor instead (blocking the way to adenosine), it has the opposite effect, causing alertness.

We 3D printed the adenosine ADORA2A receptor (PDB-ID: 3RFM) and the adenosine and caffeine molecules to show how caffeine gets in the way of adenosine in the receptor binding pocket. The STL files are available here if you want to print them to create your own demo activity.


Adenosine ModelMolecular DockingDownload
Caffeine ModelMolecular DockingDownload
Receptor ModelMolecular DockingDownload