Roberto Calandra PRO
Full Professor at TU Dresden. Head of the LASR Lab. Working in AI, Robotics and Touch Sensing.
Roberto Calandra
Facebook AI Research
RSS 2019 Workshop on Aerial Interaction and Manipulation - 23 June 2019
From the lab of Dr. Ronald Johansson, Dept. of Physiology, University of Umea, Sweden
[Allen et al. 1999]
[Chebotar et al. 2016]
[Bekiroglu et al. 2011]
[Sommer and Billard 2016]
[Schill et al. 2012]
[Yuan, W.; Dong, S. & Adelson, E. H. GelSight: High-Resolution Robot Tactile Sensors for Estimating Geometry and Force Sensors, 2017]
Collected 6450 grasps from over 60 training objects over ~2 weeks.
83.8% grasp success on 22 unseen objects
(using only vision yields 56.6% success rate)
Calandra, R.; Owens, A.; Jayaraman, D.; Yuan, W.; Lin, J.; Malik, J.; Adelson, E. H. & Levine, S.
More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch
IEEE Robotics and Automation Letters (RA-L), 2018, 3, 3300-3307
Andrew
Owens
Dinesh
Jayaraman
Wenzhen
Yuan
Justin
Lin
Jitendra
Malik
Sergey
Levine
Edward H.
Adelson
(Yes, we can)
Tian, S.; Ebert, F.; Jayaraman, D.; Mudigonda, M.; Finn, C.; Calandra, R. & Levine, S.
Manipulation by Feel: Touch-Based Control with Deep Predictive Models
IEEE International Conference on Robotics and Automation (ICRA), 2019
Sergey
Levine
Dinesh
Jayaraman
Chelsea
Finn
Stephen
Tian
Frederik
Ebert
Mayur
Mudigonda
Thank you for your attention
Lambert, N.O.; Drew, D.S.; Yaconelli, J; Calandra, R.; Levine, S.; & Pister, K.S.J.
Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning
To appear, International Conference on Intelligent Robots and Systems (IROS), 2019
By Roberto Calandra
[RSS 2019 Workshop on Aerial Interaction and Manipulation - 23 June 2019] In this talk, I will discuss what I consider two of the crucial challenges of manipulation: the use of tactile sensors, and model-based control. Humans make extensive use of touch, but integrating the sense of touch in robot control has traditionally proved to be a difficult task. As an alternative, we propose the use of data-driven machine learning methods, to learn complex multi-modal models from raw sensor measurements. I will conclude by discussing useful lessons we learned along the way, and how some of these lessons could be valuable in the context of aerial manipulation.
Full Professor at TU Dresden. Head of the LASR Lab. Working in AI, Robotics and Touch Sensing.