H (+1) 510-974-6700
B dr.rcalandra@gmail.com
Í www.robertocalandra.com
OrcID: 0000-0001-9430-8433

Roberto Calandra
Technical Expertise{ Robotics{ Model-based Control{ Tactile Sensing{ Legged Locomotion{ Machine Learning{ Reinforcement Learning{ Bayesian Optimization{ Deep Learning

Education
03 Aug 2016

Ph.D. in Computer Science, Technische Universität Darmstadt, Germany.
Thesis topic: Bayesian Modeling for Optimization and Control in Robotics
Advisor: Jan Peters (TU Darmstadt)
Instructor: Marc P. Deisenroth (Imperial College London)

19 Oct 2011

M.Sc. in Machine Learning and Data Mining, Aalto University, Finland.
Thesis topic: An Exploration of Deep Belief Networks toward Adaptive Learning
Advisor: Olli Simula (Aalto University)
Instructors: Federico Montesino Pouzols (University of Helsinki), Tapani Raiko (Aalto University)

21 Jul 2009

B.Sc. in Computer Science Engineering, Università degli Studi di Palermo, Italy.
Thesis topic: Design and Building of a Robotics Mobile Platform
Advisor: Haris Dindo (Università degli Studi di Palermo)

Work Experience
Since Oct 20182016 – 2018

Research Scientist, Meta AI (formerly Facebook AI Research), United States.
Postdoctoral Scholar, University of California: Berkeley, United States.
with Sergey Levine

Jul–Oct 2015

Research Intern, Microsoft Research, Cambridge, UK.
Advisors: Andrew Blake, Katja Hofmann

May–Dec 2010

Research Assistant, Bayesian Methodology group, Aalto University, Finland.
Advisor: Aki Vehtari

Mar–May 2008

Intern, BELTEC s.r.l., Italy.

Invited Talks at Scientific Events
04 Jun 2021

ICRA 2021 ViTac Workshop: Trends and Challenges in Visuo-Tactile Perception.
Building an Ecosystem for Research on Touch Sensing [Slides][Video]

13 Jul 2020

RSS Workshop on Visuotactile Sensors for Robust Manipulation.
Towards a Science of Touch Processing [Slides]

13 Jul 2020

RSS Workshop on Self-Supervised Robot Learning.

31 Mar 2020

ICRA Workshop on Closing the Perception-Action Loop with Vision and Tactile Sensing(ViTac 2020).
Towards In-hand Manipulation from Vision and Touch [Slides] [Video]

27 Jul 2019

Joint Statistical Meetings (JSM) - Bayesian optimization session, Denver, USA.
Bayesian Optimization for Robotics

23 Jun 2019

RSS Workshop on Aerial Interaction and Manipulation, Freiburg, Germany.
Learning Model-based Control for (Aerial) Manipulation

Roberto Calandra

1/9

20 Jun 2019

Re-Work Deep Reinforcement Learning Summit, San Francisco, USA.
Robots and the Sense of Touch

03 Jan 2019

Data, Learning and Inference (DALI), George, South Africa, Workshop on Deep Reinforcement
Learning and Robotics.
The Force Awakens: on the Importance of Force Control

08 Dec 2018

Invited panelist at the NeurIPS workshop on Meta-learning.

20 Apr 2017

Data, Learning and Inference (DALI), Tenerife, Spain, Workshop on Data Efficient Reinforcement Learning.
Goal-Driven Dynamics Learning for Model-Based RL

Invited Talks at Universities and Companies
23 Nov 2021

Samsung.
Building an Ecosystem for Research on Touch Sensing [Slides]

18 Oct 2021

Secondmind research seminar.
Bayesian Optimization for Robotics [Slides]

11 May 2021

Naver Labs, France.
Towards Embodied Intelligence

24 Mar 2021

Keele University, UK.
Towards Embodied Intelligence

21 Dec 2020

TU Dresden, Germany.
Towards Embodied Intelligence

22 Oct 2020

University of Stuttgart, Germany.
Towards Embodied Intelligence

31 Aug 2020

Max Planck Institute, Germany.
Towards Embodied Intelligence

27 May 2020

Columbia University, USA, host: Matei Ciocarlie.
Towards Embodied AI

29 Apr 2020

University of Edinburgh, UK, host: Michael Mistry.
Rethinking Model-based Reinforcement Learning

23 Oct 2019

University of California Berkeley, USA, host: Claire Tomlin.
Rethinking Model-based Reinforcement Learning

08 Oct 2019

Arizona State University, USA.
Rethinking Model-based Reinforcement Learning

31 May 2019

Stanford University, USA.

21 Feb 2018

Stanford University, USA.
Model-based Policy Search and Beyond

20 Feb 2018

NVIDIA, USA.
Learning to Grasp from Vision and Touch

26 Jan 201825 Jan 2018

TU Darmstadt, Darmstadt, Germany.
University of Freiburg, Freiburg, Germany, host: Frank Hutter.
Learning to Grasp from Vision and Touch

Max Planck Institute for Intelligent Systems, Tuebingen, Germany.
23 Jan 2018 ETH, Zurich, Switzerland, host: Aude Billard.
22 Jan 2018 EPFL, Lausanne, Switzerland.
11 Jan 2018 Università degli Studi di Palermo, Palermo, Italy.
24 Jan 2018

Roberto Calandra

2/9

20 Nov 2017

Facebook AI Research, Menlo Park, US.
Model-based Policy Search and Beyond

08 Oct 2017

University of Southern California, Los Angeles, US.
Learning to Grasp from Vision and Touch

17 Aug 2016

Max Planck Institute for Intelligent Systems, Tuebingen, Germany, host: Autonomous
Motion Department.
Robust Multi-Objective Bayesian Optimization

02 May 201616 Oct 201514 Oct 201513 Oct 2015
03 Jun 2015
02 Jun 2015
01 Apr 2015
31 Mar 201522 Dec 201424 Apr 2014
13 Nov 2013

Universität Stuttgart, Stuttgart, Germany, host: Marc Toussaint.
University College London, London, UK, host: Guy Lever.
University of Oxford, Oxford, UK, host: Michael Osborne.
Imperial College London, London, UK, host: Stefan Leutenegger, Dyson Robotics Lab.
University of British Columbia, Vancouver, Canada.
University of Washington, Seattle, US, host: Dieter Fox.
University of Freiburg, Freiburg, Germany, host: Frank Hutter.
University of Freiburg, Freiburg, Germany, host: Wolfram Burgard.
Università degli Studi di Palermo, Palermo, Italy.
Bosch Research, Stuttgart, Germany.
Imperial College London, London, UK.

Teaching
22 Feb 2022

9.357 Special Topics in Perception, MIT.
Invited Lecture on Tactile Sensing for Manipulation: Trends, Challenges, and Opportunities

25 May 2021

CS159: Advanced Topics in Machine Learning, Caltech.
Invited Lecture on Theory and Practice of Model-based Reinforcement Learning

29 Apr 2021

CS280: Computer Vision, UC Berkeley.
Invited Lecture on Perceiving, Understanding, and Interacting through Touch

13 Jan 2021

Mediterranean Machine Learning (M2L) Summer School.
Invited Lecture on Data-efficient Optimization with Bayesian Optimization

10 Apr 2020

CS188: Introduction to Artificial Intelligence, UC Berkeley.
Invited Lecture on Introduction to Bayesian Optimization

Spring 2019

AA203: Optimal and Learning-based Control, Stanford University.
Invited Lectures on Model-based Reinforcement Learning, and Model-free Reinforcement Learning

08 Aug 2018

CS189: Introduction to Machine Learning, UC Berkeley.
Invited Lecture on Bayesian Optimization

Spring 2015

Machine Learning I: Statistical Approaches, TU Darmstadt, Teaching Assistant.

Fall 2013 &Robot Learning, TU Darmstadt, Teaching Assistant.

Fall 2014

Mentorship
Graduate Students
Since 2018

Nathan Lambert, UC Berkeley, Supervised jointly with Kris Pister.

Bachelor Theses
2016

Felix Unverzagt, Modeling Robustness for Multi-Objective Optimization, TU Darmstadt.

2015

Andreas Schaefer, Prediction of Finger Flexion from ECoG Data with Deep Neural Networks,
TU Darmstadt, Supervised jointly with Jan Peters.

Roberto Calandra

3/9

2015

Dominik Pfau, Multi-Objective Optimization and Analysis of a Musculoskeletal Robot for
Bipedal Locomotion, TU Darmstadt, Supervised jointly with Katayon Radkhah.

2014

Melvin Laux, Online Feature Learning for Reinforcement Learning, TU Darmstadt.

2014

Aaron Hochlaender, Reinforcement Learning of PACMAN, TU Darmstadt, Supervised jointlywith Gerhard Neumann.

Research Projects & Internships
Cristina Pinneri, -, Facebook AI Research.
Summer 2021 Udari Madhushani, -, Facebook AI Research.
Summer 2020 Shaoxiong Wang, -, Facebook AI Research.
Fall 2021

2020

Huazhe Xu, -, Facebook AI Research.
Supervised jointly with Trevor Darrell

Alonso Marco Valle, Reinforcement Learning for Locomotion, Facebook AI Research.
Summer 2019 Stephen Tian and Brian Yang, Learning In-hand Manipulation, Facebook AI Research.
Summer 2019 Kurtland Chua, Hierarchical Reinforcement Learning, Facebook AI Research.
Summer 2019 Nathan Lambert, Objective Mismatch, Facebook AI Research.
Fall 2019

Spring 2019

Kevin Luck, Learning Morphologies and Controllers with Deep Reinforcement Learning, Facebook AI Research.

Fall 2018 –Ge Yang, Learning Plannable Representations, Facebook AI Research.

Spring 2019
Spring 2018
Summer 2017
Spring 2017
Fall 2016
Fall 2016

Thomas Liao, Joint Morphology/Controller Optimization, UC Berkeley.
Brian Yang and Grant Wang, Bayesian Optimization for Microrobot Locomotion, UC Berkeley.
Justin Lin, Learning from Touch, UC Berkeley.
Kurtland Chua, Model-based Reinforcement Learning, UC Berkeley.
Soroush Nasiriany, Automatic Reward Shaping, UC Berkeley.

F. Treede, P. Konow and M. Bied, Design of controllers for the dynamic bipedal walker FaBi,
Spring 2016 TU Darmstadt, Supervised jointly with Philipp Beckerle and Alexandra Voloshina.
Fall 2015 –Fall 2015
Fall 2014 –L. Fritsche, Learning to walk on rough terrain, TU Darmstadt.
L. Fritsche and F. Unverzagt, First-Person Tele-Operation of the iCub, TU Darmstadt.

Spring 2015
Fall 2014
Spring 2015

2014
Fall 2013

J. Geukes and M. Nakatenus, Towards Balancing with the iCub, TU Darmstadt.
G. Leser, J. Hatzenbühler, J. Schwaab and N. Eschner, Implementation and Improvementof the bipedal walking robot Fox, TU Darmstadt, Supervised jointly with Philipp Beckerle.
S. Luthardt, Deep Learning for Artificial Skin, TU Darmstadt.
M. Prediger, F. Schnell and V. Negoescu, Advanced Bayesian optimization models, TU
Darmstadt.

D. Dittmar and B. Koch, Robot learning for ball bouncing, TU Darmstadt.
Fall 2012 E. Wolter and T. Baark, Learning to bounce a ball with a robotic arm, TU Darmstadt.

Spring 2013

Professional Service
Program Chair, AISTATS 2020.
Guest Editor, JMLR Special Issue on Bayesian Optimization.
2018, 2019 Sponsorship Chair, CORL.
2022 Best Paper Award Chair, CORL.
2021, 2022 Associate Editor, ICRA.

Roberto Calandra

4/9

2017, 2021,

Associate Editor, IROS.

2022
2020
2021, 2022
2021
2022
2018

Area Chair, ICML.
Area Chair, ICLR.
Area Chair, CORL.
Area Chair, AISTATS.
Organizer/co-chair, IROS Special session on Deep Learning.

Organizer Workshops{ ICRA Workshop on Scaling Robot Learning (2022){ Facebook Workshop on The Future of Tactile Sensing: Applications and Challenges (2021){ NeurIPS Workshop on Robot Learning (2019, 2020){ NeurIPS Workshop on Meta-learning (MetaLearn) (2017, 2019, 2020){ ICLR Workshop on Task-Agnostic Reinforcement Learning (2019){ IROS Workshop on Closing the loop on Human Robot Symbiosis: Human/Robot in-the-loop
Machine Learning (2018){ RSS Workshop on Multi-Modal Perception and Control (2018){ ICML Workshop on Prediction and Generative Modeling in Reinforcement Learning (2018){ RSS Workshop on Tactile Sensing for Manipulation: Hardware, Modeling, and Learning (2017){ RSS Workshop on Learning from Demonstration in High-Dimensional Feature Spaces (2017){ NIPS Workshop on Bayesian optimization (BayesOpt) (2015, 2016)

Scientific Reviewer{ Swiss National Supercomputing Centre/ETH Zurich

Reviewer for Journals{ Journal of Machine Learning Research (JMLR){ IEEE Transactions on Robotics (T-RO){ International Journal of Robotics Research (IJRR){ Autonomous Robots (AuRo){ IEEE Robotics and Automation Letters (RA-L){ Neurocomputing{ Robotics and Autonomous Systems{ IEEE Transactions on Cybernetics{ IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics{ IEEE Transactions on Evolutionary Computation{ Optimization and Engineering (OPTE)

Reviewer for Conferences{ Neural Information Processing Systems (NeurIPS){ Robotics: Science and Systems (RSS){ International Conference on Machine Learning (ICML){ Conference on Robot Learning (CORL){ International Conference on Learning Representations (ICLR){ IEEE International Conference on Robotics and Automation (ICRA){ IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS){ International Conference on Artificial Intelligence and Statistics (AISTATS){ AAAI Conference on Artificial Intelligence (AAAI){ IEEE/RAS International Conference on Humanoid Robots (HUMANOIDS){ International Conference on Artificial Neural Networks (ICANN){ International Joint Conference on Artificial Intelligence (IJCAI){ IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
Roberto Calandra

5/9

{ IEEE International Conference on Development and Learning and on Epigenetic Robotics(ICDL-EPIROB){ IEEE International Conference on Soft Robotics (RoboSoft)

Outreach & Media Coverage{ Media outreach for the release of the touch sensing ecosystem and commercialization of thetactile sensor DIGIT [Blog Post]{ Media outreach for the launch of the FAIR Robotic Lab [Blog Post] [Fortune] [Wired] [TheVerge] [TechCrunch] [Forbes]{ Judge for the Pioneers in Engineering 2018 Robotic Competition{ Interview about our NeurIPS 2018 paper [Forbes]{ Interview about Meta-learning [Wired]{ Popular science talk on “Demystifying Robotics and AI”:
- Rotary Club of Mill Valley (17 April 2018)- Rotary Club of Sausalito (24 May 2018)- Rotary Club of Marin Sunrise (20 June 2018)- Rotary Club of Menlo Park (22 August 2018){ Popular science talk at the Rotary Club of Menlo Park (08 April 2020) on “The Importanceof Touch for Robots”

Awards & Grants
Finalist IEEE RA-L Best Paper Award
2018 NVIDIA Pioneer Award @NeurIPS
2017 Participated in the writing of the Amazon Research Award awarded to Sergey Levine ($100k)
2018

Miscellaneous
Member of the European Laboratory for Learning and Intelligent Systems (ELLIS) society
Since 2006 Member of IEEE
Since 2021 Member of Global Underwater Explorers (GUE)2003 – 2009 Proudly volunteer of AFS Italy
Since 2018

Roberto Calandra

6/9

Publications
Journals[1] S. Wang, M. Lambeta, P.-W. Chou, and R. Calandra, “TACTO: A fast, flexible, and open-source
simulator for high-resolution vision-based tactile sensors,” IEEE Robotics and Automation Letters(RA-L), vol. 7, no. 2, pp. 3930–3937, 2022. [Online]. Available: https://arxiv.org/abs/2012.08456.
DOI:10.1109/LRA.2022.3146945[2] T. Li, R. Calandra, D. Pathak, Y. Tian, F. Meier, and A. Rai, “Planning in learned latent action spaces
for generalizable legged locomotion,” IEEE Robotics and Automation Letters (RA-L), vol. 6, no. 2, pp.
2682–2689, 2021. [Online]. Available: https://arxiv.org/abs/2008.11867. DOI:10.1109/LRA.2021.3062342[3] S. Belkhale, R. Li, G. Kahn, R. McAllister, R. Calandra, and S. Levine, “Model-based meta-reinforcement
learning for flight with suspended payloads,” IEEE Robotics and Automation Letters (RA-L),
vol. 6, no. 2, pp. 1471–1478, Apr. 2021. [Online]. Available: https://arxiv.org/abs/2004.11345.
DOI:10.1109/LRA.2021.3057046[4] M. Lambeta, P.-W. Chou, S. Tian, B. Yang, B. Maloon, V. R. Most, D. Stroud, R. Santos, A. Byagowi,
G. Kammerer, D. Jayaraman, and R. Calandra, “DIGIT: A novel design for a low-cost compact
high-resolution tactile sensor with application to in-hand manipulation,” IEEE Robotics and Automation
Letters (RA-L), vol. 5, no. 3, pp. 3838–3845, 2020. [Online]. Available: https://arxiv.org/abs/2005.14679.
DOI:10.1109/LRA.2020.2977257[5] N. O. Lambert, D. S. Drew, J. Yaconelli, S. Levine, R. Calandra, and K. S. J. Pister, “Low level control
of a quadrotor with deep model-based reinforcement learning,” IEEE Robotics and Automation Letters(RA-L), vol. 4, no. 4, pp. 4224–4230, 2019. [Online]. Available: https://arxiv.org/abs/1901.03737.
DOI:10.1109/LRA.2019.2930489[6] S. Olofsson, M. Mehrian, R. Calandra, L. Geris, M. Deisenroth, and R. Misener, “Bayesian multi-objective
optimisation with mixed analytical and black-box functions: Application to tissue engineering,” IEEE Transactions on Biomedical Engineering, vol. 66, no. 3, pp. 727–739, 2018. DOI:10.1109/TBME.2018.2855404[7] R. Calandra, A. Owens, D. Jayaraman, W. Yuan, J. Lin, J. Malik, E. H. Adelson, and S. Levine, “Morethan a feeling: Learning to grasp and regrasp using vision and touch,” IEEE Robotics and Automation
Letters (RA-L), vol. 3, no. 4, pp. 3300–3307, 2018, IEEE RA-L 2018 Best Paper Award Finalist.
DOI:10.1109/LRA.2018.2852779[8] D. Buechler, R. Calandra, B. Schölkopf, and J. Peters, “Control of musculoskeletal systems using learned
dynamics models,” IEEE Robotics and Automation Letters (RA-L), vol. 3, no. 4, pp. 3161–3168, 2018.
DOI:10.1109/LRA.2018.2849601[9] B. Yang, G. Wang, R. Calandra, D. Contreras, S. Levine, and K. Pister, “Learning flexible and reusable
locomotion primitives for a microrobot,” IEEE Robotics and Automation Letters (RA-L), vol. 3, no. 3, pp.
1904–1911, 2018. DOI:10.1109/LRA.2018.2806083[10] R. Calandra, A. Seyfarth, J. Peters, and M. P. Deisenroth, “Bayesian optimization for learning gaits
under uncertainty,” Annals of Mathematics and Artificial Intelligence (AMAI), vol. 76, no. 1, pp. 5–23,
2015. DOI:10.1007/s10472-015-9463-9

Conferences[1] E. J. Smith, D. Meger, L. Pineda, R. Calandra, J. Malik, A. Romero, and M. Drozdzal, “Active 3D shapereconstruction from vision and touch,” in Advances in Neural Information Processing Systems (NeurIPS),
2021. [Online]. Available: https://arxiv.org/abs/2107.09584[2] N. Lambert, A. Wilcox, H. Zhang, K. S. J. Pister, and R. Calandra, “Learning accurate long-term
dynamics for model-based reinforcement learning,” in IEEE Conference on Decision and Control (CDC),
2021, pp. 2880–2887. [Online]. Available: https://arxiv.org/abs/2012.09156[3] M. Lambeta, H. Xu, J. Xu, P.-W. Chou, S. Wang, T. Darrell, and R. Calandra, “PyTouch: A machinelearning library for touch processing,” in IEEE International Conference on Robotics and Automation(ICRA), 2021, pp. 13 208–13 214. [Online]. Available: https://arxiv.org/abs/2105.12791[4] B. Zhang, R. Rajan, L. Pineda, N. Lambert, A. Biedenkapp, K. Chua, F. Hutter, and R. Calandra,“On the importance of hyperparameter optimization for model-based reinforcement learning,” inInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2021. [Online]. Available:
https://arxiv.org/abs/2102.13651

Roberto Calandra

7/9

[5] A. Zhang, R. McAllister, R. Calandra, Y. Gal, and S. Levine, “Learning invariant representations
for reinforcement learning without reconstruction,” in International Conference on Learning
Representations (ICLR), 2021, Oral presentation (1.8% acceptance rate). [Online]. Available:
https://arxiv.org/abs/2006.10742[6] B. Letham, R. Calandra, A. Rai, and E. Bakshy, “Re-examining linear embeddings for high-dimensional
bayesian optimization,” Advances in Neural Information Processing Systems (NeurIPS), 2020. [Online].
Available: http://arxiv.org/abs/2001.11659[7] E. J. Smith, R. Calandra, A. Romero, G. Gkioxari, D. Meger, J. Malik, and M. Drozdzal, “3D shapereconstruction from vision and touch,” in Advances in Neural Information Processing Systems (NeurIPS),
2020. [Online]. Available: https://arxiv.org/abs/2007.03778[8] S. Ebrahimi, F. Meier, R. Calandra, T. Darrell, and M. Rohrbach, “Adversarial continual learning,” inEuropean Conference on Computer Vision (ECCV), 2020.
[9] L. Pineda, S. Basu, A. Romero, R. Calandra, and M. Drozdzal, “Active MR k-space samplingwith reinforcement learning,” in International Conference on Medical Image Computing and Computer
Assisted Intervention (MICCAI), 2020, pp. 23–33. [Online]. Available: https://arxiv.org/abs/2007.10469[10] N. Lambert, B. Amos, O. Yadan, and R. Calandra, “Objective mismatch in model-based reinforcement
learning,” in Learning for Dynamics and Control (L4DC), 2020, pp. 761–770. [Online]. Available:
http://arxiv.org/abs/2002.04523[11] G. Yang, A. Zhang, A. Morcos, J. Pineau, P. Abbeel, and R. Calandra, “Plan2Vec: Unsupervised
representation learning by latent plans,” in Learning for Dynamics and Control (L4DC), 2020, pp.
935–946. [Online]. Available: https://arxiv.org/abs/2005.03648[12] A. Padmanabha, F. Ebert, S. Tian, R. Calandra, C. Finn, and S. Levine, “OmniTact: A multi-directional
high-resolution touch sensor,” in IEEE International Conference on Robotics and Automation (ICRA),
2020, pp. 618–624. [Online]. Available: https://arxiv.org/abs/2003.06965[13] T. Li, N. Lambert, R. Calandra, F. Meier, and A. Rai, “Learning generalizable locomotion skills withhierarchical reinforcement learning,” in IEEE International Conference on Robotics and Automation(ICRA), 2020, pp. 413–419. [Online]. Available: http://arxiv.org/abs/1909.12324[14] K. Luck, H. B. Amor, and R. Calandra, “Data-efficient co-adaptation of morphology and behaviour
with deep reinforcement learning,” in Conference on Robot Learning (CORL), 2019. [Online]. Available:
https://arxiv.org/abs/1911.06832[15] S. Tian, F. Ebert, D. Jayaraman, M. Mudigonda, C. Finn, R. Calandra, and S. Levine, “Manipulation
by feel: Touch-based control with deep predictive models,” in IEEE International Conference on Robotics
and Automation (ICRA), 2019, pp. 818–824.
[16] T. Liao, G. Wang, B. Yang, R. Lee, K. Pister, S. Levine, and R. Calandra, “Data-efficient learning ofmorphology and controller for a microrobot,” in IEEE International Conference on Robotics and Automation(ICRA), 2019, pp. 2488–2494.
[17] J. Lin, R. Calandra, and S. Levine, “Learning to identify object instances by touch: Tactile recognition
via multimodal matching,” in IEEE International Conference on Robotics and Automation (ICRA), 2019,
pp. 3644–3650.
[18] K. Chua, R. Calandra, R. McAllister, and S. Levine, “Deep reinforcement learning in a handful of
trials using probabilistic dynamics models,” in Advances in Neural Information Processing Systems(NIPS), 2018, pp. 4754–4765, Spotlight presentation (3.5% acceptance rate). [Online]. Available:
https://arxiv.org/abs/1805.12114[19] R. Calandra, A. Owens, M. Upadhyaya, W. Yuan, J. Lin, E. H. Adelson, and S. Levine, “The feeling of
success: Does touch sensing help predict grasp outcomes?” in Conference on Robot Learning (CORL),
2017, pp. 314–323.
[20] S. Bansal, R. Calandra, T. Xiao, S. Levine, and C. J. Tomlin, “Goal-driven dynamics learning via Bayesian
optimization,” in IEEE Conference on Decision and Control (CDC), 2017, pp. 5168–5173.
[21] S. Olofsson, M. Mehrian, L. Geris, R. Calandra, M. Deisenroth, and R. Misener, “Bayesian multi-objective
optimisation of neotissue growth in a perfusion bioreactor set-up,” in European Symposium on Computer
Aided Process Engineering, vol. 40, 2017, pp. 2155–2160.

Roberto Calandra

8/9

[22] R. Calandra, J. Peters, C. E. Rasmussen, and M. P. Deisenroth, “Manifold Gaussian processes
for regression,” in International Joint Conference on Neural Networks (IJCNN), 2016, pp. 3338–
3345, Nominated for the best student paper award at WCCI 2016. [Online]. Available:
https://arxiv.org/abs/1402.5876[23] P. Weber, E. Rueckert, R. Calandra, J. Peters, and P. Beckerle, “A low-cost sensor glove with vibrotactile
feedback and multiple finger joint and hand motion sensing for human-robot interaction,” in IEEE
International Symposium on Robot and Human Interactive Communication (RO-MAN), 2016.
[24] Z. Yi, R. Calandra, F. F. Veiga, H. van Hoof, T. Hermans, Y. Zhang, and J. Peters, “Active tactile
object exploration with Gaussian processes,” in IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS), 2016, pp. 4925–4930.
[25] R. Calandra, S. Ivaldi, M. P. Deisenroth, and J. Peters, “Learning torque control in presence of contacts
using tactile sensing from robot skin,” in IEEE-RAS International Conference on Humanoid Robots(HUMANOIDS), Nov 2015, pp. 690–695.
[26] R. Calandra, S. Ivaldi, M. P. Deisenroth, E. Rueckert, and J. Peters, “Learning inverse dynamics
models with contacts,” in IEEE International Conference on Robotics and Automation (ICRA), 2015, pp.
3186–3191.
[27] L. Fritsche, F. Unverzagt, J. Peters, and R. Calandra, “First-person tele-operation of a humanoid robot,”in IEEE-RAS International Conference on Humanoid Robots (HUMANOIDS), Nov 2015, pp. 997–1002.
[28] R. Calandra, N. Gopalan, A. Seyfarth, J. Peters, and M. P. Deisenroth, “Bayesian gait optimization for
bipedal locomotion,” in Learning and Intelligent Optimization Conference (LION), 2014, pp. 274–290.
[29] R. Calandra, A. Seyfarth, J. Peters, and M. P. Deisenroth, “An experimental comparison of Bayesian
optimization for bipedal locomotion,” in IEEE International Conference on Robotics and Automation(ICRA), May 2014, pp. 1951–1958.
[30] M. P. Deisenroth, R. Calandra, A. Seyfarth, and J. Peters, “Toward fast policy search for learning
legged locomotion,” in International Conference on Intelligent Robots and Systems (IROS), Oct 2012, pp.
1787–1792.
[31] R. Calandra, T. Raiko, M. P. Deisenroth, and F. Montesino Pouzols, “Learning deep belief networks from
non-stationary streams,” in International Conference on Artificial Neural Networks (ICANN), 2012, pp.
379–386.

Pre-prints[1] D. Buchler, S. Guist, R. Calandra, V. Berenz, B. Scholkopf, and J. Peters, “Learning toplay table tennis from scratch using muscular robots,” Under review, 2021. [Online]. Available:
https://arxiv.org/abs/2006.05935[2] L. Pineda, B. Amos, A. Zhang, N. O. Lambert, and R. Calandra, “MBRL-Lib: A modular library formodel-based reinforcement learning,” Arxiv, 2021. [Online]. Available: https://arxiv.org/abs/2104.10159[3] H. Xu, Y. Luo, S. Wang, T. Darrell, and R. Calandra, “Towards learning to play piano with dexterous
hands and touch,” Under Review, 2021. [Online]. Available: https://arxiv.org/abs/2106.02040[4] D. Buchler, R. Calandra, and J. Peters, “Learning to control highly accelerated ballistic movements on
muscular robots,” Under review, 2019. [Online]. Available: http://arxiv.org/abs/1904.03665

Roberto Calandra

9/9