Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets

Imitation learning has traditionally been applied to learn a single task from demonstrations thereof. The requirement of structured and isolated demonstrations limits the scalability of imitation learning approaches as they are difficult to apply to real-world scenarios, where robots have to be able to execute a multitude of tasks. In this paper, we propose a multi-modal imitation learning framework that is able to segment and imitate skills from unlabelled and unstructured demonstrations by learning skill segmentation and imitation learning jointly. The extensive simulation results indicate that our method can efficiently separate the demonstrations into individual skills and learn to imitate them using a single multi-modal policy. The video of our experiments is available at http://sites.google.com/view/nips17intentionganComment: Paper accepted to NIPS 201

GUARDIANS final report part 1 (draft): a robot swarm assisting a human fire fighter

Emergencies in industrial warehouses are a major concern for fire fighters. The large dimensions together with the development of dense smoke that drastically reduces visibility, represent major challenges. The Guardians robot swarm is designed to assist re ghters in searching a large warehouse. In this paper we discuss the technology developed for a swarm of robots assisting re ghters. We explain the swarming algorithms which provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also the means to locate the robots and humans. Thus the robot swarm is able to provide guidance information to the humans. Together with the fire fighters we explored how the robot swarm should feed information back to the human fire fighter. We have designed and experimented with interfaces for presenting swarm based information to human beings

Modeling the power consumption of a Wifibot and studying the role of communication cost in operation time

Mobile robots are becoming part of our every day living at home, work or entertainment. Due to their limited power capabilities, the development of new energy consumption models can lead to energy conservation and energy efficient designs. In this paper, we carry out a number of experiments and we focus on the motors power consumption of a specific robot called Wifibot. Based on the experimentation results, we build models for different speed and acceleration levels. We compare the motors power consumption to other robot running modes. We, also, create a simple robot network scenario and we investigate whether forwarding data through a closer node could lead to longer operation times. We assess the effect energy capacity, traveling distance and data rate on the operation time