Publications (including pre-prints)
- F. Shkurti, W.-D. Chang, P. Henderson, M. J. Islam, J. Camilo Gamboa Higuera, J. Li, T. Manderson, A. Xu, G. Dudek, and J. Sattar, “Underwater Multi-Robot Convoying using Visual Tracking by Detection,” in Proc. of The International Conference on Intelligent Robots and Systems (IROS) (in review), 2017.
- I. V. Serban, R. Lowe, P. Henderson, L. Charlin, and J. Pineau, “A survey of available corpora for building data-driven dialogue systems,” arXiv preprint arXiv:1512.05742, 2015 [Online]. Available at: https://arxiv.org/pdf/1512.05742.pdf
- P. Henderson, “Implanted intracortical electrodes as chronic neural interfaces to the central nervous system,” 2015 [Online]. Available at: https://peerj.com/preprints/1255v1.pdf
- P. Henderson and M. Vertescher, “An Analysis of Parallelized Motion Masking Using Dual-Mode Single Gaussian Models,” arXiv preprint arXiv:1702.05156, 2017 [Online]. Available at: https://arxiv.org/pdf/1702.05156.pdf
- P. Henderson and M. Maheswaran, “Chaotic Memory Randomization for Securing Embedded Systems,” arXiv preprint arXiv:1611.00742 [Online]. Available at: https://arxiv.org/pdf/1611.00742.pdf
Underwater Multi-Robot Convoying using Visual Tracking by Detection
We present a robust multi-robot convoying approach relying on visual detection of the leading agent, thus enabling target following in unstructured 3D environments. Our method is based on the idea of tracking by detection, which interleaves image-based position estimation via temporal filtering with efficient model-based object detection. This approach has the important advantage of mitigating tracking drift (i.e. drifting out of the view of the target), which is a common symptom of model-free trackers and is detrimental to sustaining convoying in practice. To illustrate our solution, we collected extensive footage of an underwater swimming robot in ocean settings, and hand-annotated its location in each frame. Based on this dataset, we present an empirical comparison of multiple tracker variants, including the use of several Convolutional Neural Networks both with and without recurrent connections, as well as frequency-based model-free trackers. We also demonstrate the practicality of this tracking-by-detection strategy in real-world scenarios, by successfully controlling a legged underwater robot in five degrees of freedom to follow another robot's arbitrary motion.
Download: Paper [In Review]
View: Project Website
A survey of available corpora for building data-driven dialogue systems
During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective.
View: Project Website
Autonomous Swarm Behaviour in Mesh Networked Agents
In recent years, unmanned aerial vehicles (UAVs) have become commonplace. They have been used most notably for military purposes, film making, and more recently, parcel delivery. However, current UAV technologies focus on piloted systems. While some focus has been shifted toward fully autonomous systems with large companies like Google and Amazon investing heavily in such tech- nologies, there is still much technology to be developed for these autonomous systems to become commonplace. Most important and lacking is the flight time of these systems. Current battery technologies cannot sustain motor function for extensive flight times, particularly in multi-copter systems, as such these systems still cannot accomplish the tasks they need without manual super- vision to travel safely and recharge. This restriction comes into play heavily with mapping and exploration tasks, which can be beneficial for many industries, particularly agriculture. However, recent developments in coordinated systems of multiple autonomous agents have shown that co- ordinated tasks can be accomplished at a much faster pace and in the available amount of flight time. As such, we pursue a multi agent system interconnected with mesh networking technologies, which can pursue a mapping and exploration task in a fully decentralized and self-coordinated autonomous manner. While a complete system including hardware may not be feasible during the year, we plan to design a software system and choose appropriate technologies to accomplish this task. Additionally, our goal is to run our designed software system and algorithms in simulation to show its feasibility and the agents’ ability to autonomously make decisions in a decentralized fashion. During this semester, we designed such a system and its requirements, researched the appropriate technologies, and began work on testing basic communication amongst hardware nodes. Finally, we began the development of a simulator which we could test our algorithms in.
Download: Technical Report I
Download: Technical Report II
Watch: Brief 3D Demo
Implanted Intracortical Electrodes as Chronic Neural Interfaces to the Central Nervous System
Recent developments in neural interfaces show that it is possible to have fine control of a robotic prosthetic by interfacing with the motor cortex of the human brain. Development of long term systems for this purpose is a challenging task with many different possibilities. Intracortical implants have shown the most promise in providing enough signal selectivity and throughput for complex control systems with many degrees of freedom. Intracortical systems generally fall into two categories: MEMS devices and bundle of wire systems. While both show promise, MEMS systems have been greatly popularized due to their reproducibility. In particular, the Michigan probe and Utah microarray are often used as a base for construction of more complex intracortical systems. However, these systems still carry many downsides. Their long-term viability is questionable, with mixed results. The effects of damage from implantation are still inconclusive and immune responses remain a problem for long-term use. While there is some promising research in the use of bioactive molecules and biocompatible materials to prevent immune responses, more controlled study is needed before intracortical systems become widespread.