APRIL

The Active Perception and Robot Interactive Learning (APRIL) laboratory focuses on the research and development of intelligent robotic technologies to drive breakthrough research to enable the next generation of robots to perform complex manipulation tasks, work alongside safely and robustly with humans, as well as transform industries such as manufacturing, logistics, healthcare, agri-food, and more. It is believed that robots should be able to learn new skills by interacting with humans and perceiving the environments using modern robot vision and learning techniques. This is a highly AI and data technology oriented robotics research group working towards the creation of novel and reproducible technologies that robot can harmoniously live with human beings.

The overall research theme involves the creation of various robot perception and manipulation systems to augment robot capabilities of working in complex and dynamical environments. This includes research in areas such as robot active perception, robot learning and manipulation, robot deep reinforcement learning, robot sim2real learning, as well as development of novel robot mobile manipulation systems including smart end-effectors, sensing modules.

Laboratories

The APRIL Laboratory is part of the Advanced Robotics Department and counts with a range of state-of-the-art equipment that support our research and stimulate the curiosity of our researchers. The equipment includes a customized robot mobile manipulator, Franka Emika robot arm, Kinova GEN3 robot arm, Schunk robot arm, robot grippers, dexterous robot hands, F/T sensors, all kinds of cameras, server with high-performance GPU farms, etc. The researchers come from multi-disciplinary backgrounds, including computer science, robotics, mechatronics, electronics.

Activities

The APRIL Laboratory’s research and development work is driven by several EU and national projects.

The research topics (PhD themes) include but not limited to:

  • Learning control for fine manipulation tasks
  • Sim2Real DRL for robot motion planning and control
  • Deformable objects perception, modelling and manipulation
  • Optimal action-perception coupling for mobile manipulation

Projects include:

  1.  AutoMAP: AutoMAP (EU FP7 EUROC AutoMAP) addresses applications of robotic mobile manipulation in unstructured environments as found at CERN. This project is based on use case operations to be carried out on CERN’s flagship accelerator, the Large Hadron Collider. The main objective is to carry out the maintenance work using a remotely controlled robot mobile manipulator to reduce maintenance personnel exposure to hazards in the LHC tunnels – such as ionising radiation and oxygen deficiency hazards. A second goal is to allow the robot being able to autonomously carry out the same tasks in the assembly facility as in the tunnel on collimators during their initial build and quality assurance through the robot learning technologies.
  2.  Learn-Real: LEARN-REAL (EU H2020 Chist-Era Learn-Real) proposes to learn manipulation skills through simulation for object, environment and robot, with an innovative toolset comprising: i) a simulator with realistic rendering of variations allowing the creation of datasets and the evaluation of algorithms in new situations; ii) a virtual-reality interface to interact with the robots within their virtual environments, to teach robots object manipulation skills in multiple configurations of the environment; and iii) a web-based infrastructure for principled, reproducible and transparent benchmarking of learning algorithms for object recognition and manipulation by robots. Strong AI oriented technologies (Deep Reinforcement Learning, Deep Learning, Sim2Real transfer learning) are our main concerns in this project.
  3.  VINUM: VINUM, namely Grape Vine Recognition, Manipulation and Winter Pruning Automation (VINUM), is an Agri-Food project funded by the IIT-Unicatt Joint Lab. The objective is to apply the state-of-the-art mobile manipulation platforms and systems, a wheeled mobile platform with a commercial full torque sensing arm and multiple sensors, and an under-develop quadruped robot mobile platform with a customized robotic arm and multiple sensors (Cooperated with DLS research line led by Dr. Claudio Semini, for various maintenance and automation work in the vineyard, e.g., pruning, inspecting to tackle the shortage of skilled workers. Together with Learn-Real project, VINUM is targeting at providing very robust solutions for outdoor application to deal with all kinds of natural objects.

Collaborations

We welcome all kinds of collaborations to speed-up the progress of intelligent robotic technologies and to achieve meaningful results. Therefore, we are always open to new and challenging opportunities to collaborate with other groups and institutions. We are also open to interested students and scholars who want to spend some short time for visiting or longer time for PhD or Post-doc in the group. In the past years, we have collaborated in the robotics area with: German Aerospace Center (Germany), European Nuclear Research (Switzerland), Prisma Lab @ Università degli Studi di Napoli Federico II (Italy), Robot Learning & Interaction Group @ Idiap research institute (Switzerland), Department of Sustainable Crop Production @ Università Cattolica del Sacro Cuore (Italy), Robotics Institute @ Shenzhen Academy of Aerospace Technology (China), Department of mathmetics @ École Centrale de Lyon (France).

Lead Researcher: Fei Chen