Mikhail Frank

About

Mikhail Frank is a robotics and artificial intelligence researcher whose work sits at the intersection of humanoid robotics, computer vision, and machine learning. His research has made significant contributions to motion planning, cognitive robotics, and intrinsic motivation in autonomous systems. Frank's most influential work, "Curiosity Driven Reinforcement Learning for Motion Planning on Humanoids" (2014, 75 citations), advanced the field of artificial curiosity by moving beyond toy scenarios to demonstrate intrinsic motivation on complex, high-degree-of-freedom humanoid platforms — a notable leap from prior theoretical work. His task-relevant roadmap framework introduced a flexible, sampling-based approach to humanoid motion planning, while his modular behavioral environment (MoBeE) provided an integrated architecture enabling robots to see, act, and react cohesively. Frank also made meaningful strides in robot perception, developing methods for spatial object localization, autonomous visual learning, and hand detection on the iCub humanoid. His icVision framework became a practical tool for rapid prototyping in cognitive robotics research. Collectively, his publications reflect a sustained effort to bridge theoretical machine learning with real-world humanoid embodiment, making him a notable contributor to developmental and cognitive robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   Curiosity Driven Reinforcement Learning for Motion Planning on Humanoids

   75 citations · 2014
2. 2
   Task-relevant roadmaps: A framework for humanoid motion planning

   20 citations · 2013
3. 3
   The Modular Behavioral Environment for Humanoids and other Robots (MoBeE)

   18 citations · 2012
4. 4
   Transferring spatial perception between robots operating in a shared workspace

   17 citations · 2012
5. 5
   Learning Spatial Object Localization from Vision on a Humanoid Robot

   16 citations · 2012
6. 6
   An Integrated, Modular Framework for Computer Vision and Cognitive Robotics Research (icVision)

   14 citations · 2012
7. 7
   Humanoid learns to detect its own hands

   13 citations · 2013
8. 8
   Autonomous learning of robust visual object detection and identification on a humanoid

   10 citations · 2012
9. 9
   icVision: A modular vision system for cognitive robotics research

   9 citations · 2012
10. 10
    AutoIncSFA and vision-based developmental learning for humanoid robots

    8 citations · 2011

Key Collaborators

Contact & Links