Fig. 1. Andrea, May 2015.
Other portraits by or with robots.

Andrea Censi

I am Chief Technology Officer of Duckietown Engineering co.

FAQ: can we acquire you? Answer: no - this one is a fictional company used to teach MIT 2.166 Autonomous Vehicles

I am also a Research Scientist and PI at LIDS (Laboratory for Information and Decision Systems) at MIT. Please do not introduce me as the “director” of LIDS. It's called a “laboratory” but it's not my laboratory. PhD Comics thinks it's because we have a relatively small ego for the size. The director of LIDS is Asu Ozdaglar.

I obtained a Ph.D. in Control & Dynamical Systems from Caltech in 2012. [Whether they regret it or not is unclear.

Vital statistics

Count Age

Fig. 2. Stress and procrastination levels in the last 7 days (updated Tue, 03 Jan 2017 11:00 PST). Stress is mea­su­red by the num­ber of flag­ged mes­sa­ges in the email in­box; pro­cra­sti­na­tion is mea­su­red by the me­dian age of tho­se mes­sa­ges (here's the code to make your own).


censi (at) (PGP key)
(626) 872-3674
room 32-D558 (Stata Center)
office #
(617) 452-2351
32 Vassar St
room 32-D558
Cambridge, MA 02139

What's new

Upcoming travel

ICRA 2016

My research

Here's what's up: The robots are coming! Please see here to check the likelihood that your job will be replaced by a machine. But don't worry too much; the thing is, we don't really know how to design complex robotic systems, despite the headlines.

I work on the Science of Embodied Autonomy. The engineering applications of my work are towards making complex autonomous robotic systems more robust, more efficient, and easier to design. More generally, I want to understand what are the principles underlying embodied intelligence, both natural and artificial.

1. Co-Design

I am working on a new theory of “co-design” to unify all aspects of the design of robotic systems, including energetics, actuation, sensing, and computation.

For more information, please see the site

A.C.. A mathematical theory of co-design. Technical Report, Laboratory for Information and Decision Systems, MIT, September 2016. pdf supp. materialbibtex

A.C.. Handling uncertainty in monotone co-design problems. Technical Report, Laboratory for Information and Decision Systems, MIT, October 2016. pdf supp. materialbibtex

2. Neuromorphic / bio-inspired control

I'm interested in co-design problems that couple sensing, computation, and actuation in a non-trivial way, especially from the point of view of design minimality and "joint inference and control".

In particular, I'm interested in the robotics applications of event-based neuromorphic vision sensors. These are a new kind of sensor that outputs a low-latency stream of eevents, generated every time there is a change in the local brightness perceived by a pixel, rather than a periodic series of frames.

Events from a neuromorphic sensor, in real time (left) and slowed down 50x (center). On the right, events superimposed with a CMOS sensor's output.

My research is guided by the questions:

  • Theory: How can we formally say that this sensor class is better than another? For what tasks? In what environments?
  • Algorithms: How can we obtain "zero-latency" event-based controllers?
  • Systems: How can we integrate these sensors in existing control architectures?

A.C.. Efficient Neuromorphic Optomotor Heading Regulation. In American Control Conference (ACC). Chicago, IL, June 2015. bibtex

A.C., Erich Mueller, Emilio Frazzoli, and Stefano Soatto. A Power-Performance Approach to Comparing Sensor Families, with application to comparing neuromorphic to traditional vision sensors. In IEEE International Conference on Robotics and Automation (ICRA). May 2015. bibtex

A.C. and Davide Scaramuzza. Low-latency event-based visual odometry. In IEEE International Conference on Robotics and Automation (ICRA). May 2014. pdf supp. material slidesbibtex

A.C., Jonas Strubel, Christian Brandli, Tobi Delbruck, and Davide Scaramuzza. Low-latency localization by active led markers tracking using a dynamic vision sensor. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 891–898. Tokyo,Japan, November 2013. pdfdoi supp. material slidesbibtex

Sawyer B. Fuller, Michael Karpelson, A.C., Kevin Y. Ma, and Robert J. Wood. Controlling free flight of a robotic fly using an onboard vision sensor inspired by insect ocelli. Journal of the Royal Society Interface, August 2014. video supp. materialbibtex

The world is full of natural robots, which are informally called “animals.” I have worked on the identification of fruit-fly stimulus-elicited behavior, with Dickinson and Straw. This kind of work is interesting for an engineer, because it can be seen as reverse-engineering of an existing solution. Lately, I'm thinking about how to make this duality between analysis (biology) and synthesis (robotics) more formal. I'm very interested in possible collaborations on this topic.

A.C.*, Andrew D. Straw*, Rosalyn W. Sayaman, Richard M. Murray, and Michael H. Dickinson. Discriminating external and internal causes for saccade initiation in freely flying Drosophila. PLOS Computational Biology, February 2013. pdfdoi supp. material slidesbibtex

Some recent workshops on these topics:

3. Sensorimotor Learning and Bootstrapping

Imagine you are a brain-in-a-vat that wakes up connected to an unknown body through two streams of uninterpreted observationd and commands, without any prior knowledge of the sensors, actuators or environment. Would you be able to learn a model of your body and use it to perform useful tasks? This is the "bootstrapping scenario": the learning problem for an embodied agent in the limit of prior knowledge tending to zero.

Uninterpreted streams of observations and commands from a robotic sensorimotor cascade.

My research in this field is guided by the questions:

  • Theory: How much prior knowledge is needed by an agent?
  • Algorithmics: What are tractable classes of models for sensorimotor learning?
  • Systems: How can we introduced learning and adaptivity functionality in traditional robotic control systems?

The video shows learning of a bilinear model of a sensorimotor cascade for a camera. The agent starts with no previous knowledge on the sensor geometry, and by correlating observations with commands, it can learn a generative model for the data. The same model can be used for learning the dynamics of different sensors (range-finder, camera, field sampler). See many other videos of related experiments.

Representative works:

A.C. and Richard M. Murray. Bootstrapping bilinear models of Simple Vehicles. International Journal of Robotics Research, 34:1087–1113, July 2015. pdfdoi video supp. material slidesbibtex

A.C. and Davide Scaramuzza. Calibration by correlation using metric embedding from non-metric similarities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35:2357–2370, 10 2013. pdfdoi supp. materialbibtex

A.C., Adam Nilsson, and Richard M. Murray. Motion planning in observations space with learned diffeomorphism models. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2860–2867. Karlsruhe, Germany, 5 2013. pdfdoi supp. materialbibtex

A.C. and Richard M. Murray. Learning diffeomorphism models of robotic sensorimotor cascades. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Saint Paul, MN, May 2012. pdfdoi supp. material slidesbibtex

A.C. and Richard M. Murray. Uncertain semantics, representation nuisances, and necessary invariance properties of bootstrapping agents. In Joint IEEE International Conference on Development and Learning and Epigenetic Robotics. Frankfurt, Germany, August 2011. pdfdoi slidesbibtex

A.C., Antonio Franchi, Luca Marchionni, and Giuseppe Oriolo. Simultaneous calibration of odometry and sensor parameters for mobile robots. IEEE Transactions on Robotics, 29(2):475–492, April 2013. pdfdoi supp. materialbibtex

My dissertation:

A.C.. Bootstrapping Vehicles: a formal approach to unsupervised sensorimotor learning based on invariance. Technical Report, California Institute of Technology, 2012. pdf supp. material slidesbibtex

Recent workshop:

4. “Classic” robotics perception

At the beginning of my research career I worked on what we can now call “classic” robotics perception and planning problems.

Some recent work on the problem of pose-graph optimization, with Luca Carlone:

Luca Carlone and A.C.. From Angular Manifolds to the Integer Lattice: Guaranteed Orientation Estimation with Application to Pose Graph Optimization. IEEE Transactions on Robotics, April 2014. pdfdoi supp. material slidesbibtex

Luca Carlone, A.C., and Frank Dellaert. Coherent measurements selection via l1 relaxation: an approach to robust estimation over graphs. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). October 2014. bibtex

Some papers on the algorithmics and the accuracy of pose tracking and localization using range data:

A.C.. On achievable accuracy for pose tracking. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Kobe, Japan, May 2009. pdfdoi supp. material slidesbibtex

A.C.. An ICP variant using a point-to-line metric. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Pasadena, CA, May 2008. pdfdoi supp. material slidesbibtex

A.C.. An accurate closed-form estimate of ICP's covariance. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 3167–3172. Rome, Italy, April 2007. pdfdoi supp. material slidesbibtex

A.C.. On achievable accuracy for range-finder localization. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 4170–4175. Rome, Italy, April 2007. pdfdoi supp. material slidesbibtex

Point-to-point ICP (left) vs point-to-line ICP (right).


Most of my papers come with software and datasets in the spirit of reproducible research (subject to time constraints...). These are the software packages that were polished and documented enough for wider use.

Software users: It's always great to know that my software is used for something cool. Please send me an email if you do.

Some general-purpose software that came out as a side-effect of my research:

A.C.. Compmake: Minimal Effort Parallelization and Job Management for Python Applications. Technical Report, Laboratory for Information and Decision Systems/MIT, October 2014. pdf supp. materialbibtex

Some robotics-specific software packages:

Professional Service


I am originally from Rome, Italy.

The ICRA 2015 trailer