Roberto Calandra is a Research Scientist at Facebook AI Research. Previously, he was a Postdoctoral Scholar at the University of California, Berkeley (US) in the Berkeley Artificial Intelligence Research Laboratory (BAIR) working with Sergey Levine. His education includes a Ph.D. from TU Darmstadt (Germany) under the supervision of Jan Peters and Marc Deisenroth, a M.Sc. in Machine Learning and Data Mining from the Aalto university (Finland), and a B.Sc. in Computer Science from the Università degli studi di Palermo (Italy).
My scientific interests focus at the conjunction of Machine Learning and Robotics, in what is know as Robot Learning. Some of the research topics that I am currently developing include: Deep Reinforcement Learning, Model-based RL, Tactile Sensing, Dynamics Modeling, and Bayesian Optimization.
- 20 Jun 2019: Re-Work Deep Reinforcement Learning Summit – Robots and the Sense of Touch
- 23 Jun 2019: RSS 2019 - Workshop on Aerial Interaction and Manipulation – Learning Model-based Control for (Aerial) Manipulation
- 27 Jul 2019: Joint Statistical Meetings (JSM) - Bayesian optimization session – Bayesian Optimization for Robotics
- 31 May 2019: Stanford University
- 03 Jan 2019: DALI
- 21 Feb 2018: Stanford University
- 26 Jan 2018: TU Darmstadt
- 25 Jan 2018: University of Freiburg
- 24 Jan 2018: Max Planck Institute (Tuebingen)
- 23 Jan 2018: ETH
- 22 Jan 2018: EPFL
- 11 Jan 2018: Università di Palermo
- 08 Oct 2017: University of Southern California
- 03 Jun 2019: I am guest-teaching two lectures on reinforcement learning at Stanford University in the AA203: Optimal and Learning-based Control course.
- 16 May 2019: We just released a blog post about robotics at Facebook AI Research. [Fortune] [Wired] [The Verge] [TechCrunch]
- 07 May 2019: Our paper More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch has been nominated a finalist for the best RA-L 2018 paper award.
- 09 Apr 2019: New pre-print on Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots .
- 08 Mar 2019: New pre-print on Fast Neural Network Verification via Shadow Prices.
- 28 Jan 2019: Three papers accepted at ICRA: Data-efficient Learning of Morphology and Controller for a Microrobot with Thomas, Grant, Brian, Rene, Kris, and Sergey; Learning to Identify Object Instances by Touch: Tactile Recognition via Multimodal Matching with Justin and Sergey; Manipulation by Feel: Touch-Based Control with Deep Predictive Models with Stephen, Frederik, Dinesh, Mayur, Chelsea, and Sergey.
- 11 Jan 2019: New pre-print on Low Level Control of a Quadrotor with Deep Model-Based Reinforcement learning.
- 13 Dec 2018: I am co-organizing an ICLR workshop on Task-agnostic Reinforcement Learning together with Danijar Hafner, Amy Zhang, Ahmed Touati, Deepak Pathak, Frederik Ebert, Rowan McAllister, Marc G. Bellemare, Raia Hadsell, Alessandro Lazaric, Joelle Pineau. Deadline for submission is 29 March 2019.
- 04 Dec 2018: Our paper Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models has been selected for one of the NVIDIA Pioneer Awards at NeurIPS.
- October 2018: I am happy to announce that I joined Facebook AI Research as a Research Scientist.
- 07 Sep 2018: Our Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models [Website] paper has been accepted at NIPS 2018 as a spotlight presentation (~4% acceptance rate). Congratulations to Kurtland for his first paper!
- 01 Aug 2018: Three journal published. Congratulations to all the authors! More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch with Andrew, Dinesh, Wenzhen, Justin, Jitendra, Ted, and Sergey; Control of Musculoskeletal Systems using Learned Dynamics Models with Dieter, Bernhard and Jan; Bayesian Multi-Objective Optimisation with Mixed Analytical and Black-Box Functions: Application to Tissue Engineering with Simon, Mohammad, Liesbet, Marc, and Ruth.
- 30 May 2018: New pre-print available on arxiv about model-based RL: Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models [Website]
- 28 May 2018: New pre-print available on arxiv about learning to grasp with tactile sensing: [More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch](http://arxiv.org/abs/1805.11085) [[Website](https://sites.google.com/view/more-than-a-feeling)]
- 06 Apr 2018: I am co-organizing the [RSS Workshop on Multi-Modal Perception and Control](https://sites.google.com/view/mmpc2018) together with Filipe Veiga, Aude Billard and Jan Peters. The submission deadline will be in May 2018!
- 22 Feb 2018: I am co-organizing the [FAIM Workshop on Prediction and Generative Modeling in Reinforcement Learning](http://reinforcement-learning.ml/pgmrl2018) together with Matteo Pirotta, Sergey Levine, Martin Riedmiller and Alessandro Lazaric. The submission deadline is 01 June 2018!
- 26 Jan 2018: [Learning Flexible and Reusable Locomotion Primitives for a Microrobot](/papers/2018_yang_ral.pdf) accepted at RAL+ICRA. Congratulations to Brian and Grant!
- 20 Jan 2018: I participated to the Dagstuhl seminar on [Personalized Multiobjective Optimization](http://www.dagstuhl.de/de/programm/kalender/semhp/?semnr=18031).
- 03 Jan 2018: The talks from the [RSS17 Workshop on Tactile Sensing for Manipulation: Hardware, Modeling, and Learning](https://sites.google.com/view/rss17ts/) are now [available online](https://www.youtube.com/playlist?list=PLXM0QqL8gqxvrJD9A78zHz_eOmGL9NZND)
- 30 Nov 2017: Three papers accepted to the [NIPS workshop on Acting and Interacting in the Real World](https://sites.google.com/view/nips17robotlearning/) which will take place on Dec. 8th at NIPS: [More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch](https://drive.google.com/open?id=1K2DORnCdwh7vik5oUIvnPLzyvkfMKgEE), [Learning Flexible and Reusable Locomotion Primitives for a Microrobot](https://drive.google.com/open?id=1vTbQY7nkodUiK4PD6Y71-bBJsjEsoEHV), and [On the Importance of Uncertainty for Control with Deep Dynamics Models](https://drive.google.com/open?id=1zdTLzufnzYzi7yktvkSEfNKZ3o8yqioV)
- 20 Nov 2017: Invited talk today at Facebook on "Model-based Policy Search and Beyond"
- 16 Oct 2017: [The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?](https://arxiv.org/abs/1710.05512) is now on available on arxiv.
- 04 Oct 2017: Invited talk today at the University of Southern California on "Learning to Grasp from Vision and Touch".
- Sep 2017: I am organizing the [NIPS Workshop on Meta-learning (MetaLearn)](http://metalearning.ml/) together with Frank Hutter, Hugo Larochelle, and Sergey Levine. The submission deadline is 01 November 2017!
- Sep 2017: Two papers accepted at [CoRL 2017](http://www.robot-learning.org/): [The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?](https://arxiv.org/abs/1710.05512), and [MBMF: Model-Based Priors for Model-Free Reinforcement Learning](http://arxiv.org/abs/1709.03153).
- Aug 2017: I am editing the [JMLR Special issue on Bayesian optimization](http://bayesopt.github.io/special-issue.html) together with Roman Garnett, Javier González, Frank Hutter, and Bobak Shahriari. The deadline for submissions is 31 March 2018!
- Jul 2017: Our paper Goal-Driven Dynamics Learning via Bayesian Optimization has been accepted at CDC.
- Apr 2017: Today I gave a talk about “Goal-Driven Dynamics Learning for Model-Based RL” at the DALI 2017 Workshop on Data-Efficient Reinforcement Learning. [Slides][Video]
Deep reinforcement learning in a handful of trials using probabilistic dynamics models K Chua, R Calandra, R McAllister, S Levine Advances in Neural Information Processing Systems, 4754-4765
Bayesian Optimization for Learning Gaits under Uncertainty R Calandra, A Seyfarth, J Peters, MP Deisenroth Annals of Mathematics and Artificial Intelligence (AMAI) 76 (1), 5–23
More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch R Calandra, A Owens, D Jayaraman, W Yuan, J Lin, J Malik, EH Adelson, … IEEE Robotics and Automation Letters 3 (4), 3300–3307 [arxiv] [Project website]