About

Roberto Calandra is a prominent robotics and machine learning researcher whose work sits at the intersection of tactile sensing, robot manipulation, locomotion, and probabilistic learning methods. He has made transformative contributions to the field of robotic touch, pioneering the use of vision-based tactile feedback to enable robots to grasp, regrasp, and manipulate objects with human-like dexterity. His 2018 paper "More Than a Feeling" (353 citations) demonstrated that integrating tactile and visual inputs dramatically improves robotic grasping, while his development of TACTO (131 citations), an open-source tactile sensor simulator, has lowered barriers for the broader research community to explore touch-based robotics. Beyond manipulation, Calandra has advanced Bayesian optimization for robot locomotion, showing how data-efficient probabilistic methods can optimize bipedal and quadrupedal gaits under real-world uncertainty, with multiple papers accumulating hundreds of citations. His work on Manifold Gaussian Processes (214 citations) further demonstrates his depth in probabilistic modeling. From teaching robots to play table tennis to enabling aerial vehicles to adapt to suspended payloads, Calandra's research consistently bridges rigorous theory with real-world robotic challenges, earning him recognition as a leading voice in embodied machine learning.

Research Focus

Key Achievements

25
H-Index
50
Papers
2,485
Total Citations
50
Avg Citations/Paper
🏆 Most Cited Paper
More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch
353 citations · 2018
📈 Most Prolific Year: 2019 (10 Papers)
🤝 Key Collaborators: 137
🏛 Institutions: University of California, Berkeley, Technische Universität Darmstadt, Alpha Omega Alpha Medical Honor Society, Menlo School, University of Canberra, Deutsche Telekom (Slovakia)

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago