UC Berkeley's AUTOLAB, directed by Professor Ken Goldberg, is a
leading center for research in robotics and automation sciences. The
lab supports approximately 30 postdocs, PhD students, and undergrads
pursuing projects in Robust Robot Grasping and Manipulation for
Warehouses, Homes, and Robot-Assisted Surgery. We focus on methods at
the intersection of analytic theory (eg, geometric and statistical
models, motion planning) and deep learning.
AUTOLAB Research Papers: http://goldberg.berkeley.edu/pubs/
Virtual-Lab-Tour:
https://drive.google.com/open?id=17o7RpbO7eb0O772fITOQOnImCUv5BNVa
Our research is sponsored in part by NSF, NDSEG, USDA, Google,
Siemens, HP, Honda, Autodesk, Toyota Research Institute, Amazon
Robotics, Intel, Samsung, Cisco, and Intuitive Surgical. The AUTOLAB
is located in 2111 Etcheverry Hall and 8015 Berkeley Way West, UC
Berkeley.
A Sample of Ongoing Projects:
Dex-Net for Robust Robot Grasping. Combining analytic mechanics theory with
Deep Learning using sim2real at scale facilitates highly efficient,
robust, grasping that generalizes surprisingly well to new objects.
Cloud Robotics. We are exploring how the ability to access remote
data, code, and processing can facilitate scalable robot learning.
Safety Augmented Value Estimation from Demonstrations (SAVED): Safe
Deep Model-Based RL for Sparse Cost Robotic Tasks. Addressing the
difficulty in hand-engineering a dense cost function, which can lead
to unintended behavior, and dynamical uncertainty, which makes
exploration and constraint satisfaction challenging.
Deep Learning from Demonstrations. We are developing new methods
where humans can efficiently teach robots to robustly perform tasks
such as surgical needle insertion, grasping in clutter, part
singulation, rope-tying and assembly.
Robot-Assisted Surgery. We're developing new geometric models and
algorithms for automating surgical subtasks such as suturing and
debridement using the da Vinci surgical robot.
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