UC Berkeley Robotics

Learning 3D without direct 3D supervision. Great exposition by Shubham Tulsiani and Tinghui Zhou from the Berkeley AI Research lab!

The Confluence of Geometry and Learning The BAIR Blog

Trained all in simulation, this robot has learned to reliably grasp a wide range of real objects! (By the Goldberg lab at UC Berkeley.)

The most dexterous robot yet learned to grasp from virtual objects A dexterous multi-fingered robot practiced using virtual objects in a simulated world, showing how machine learning and the cloud could revolutionize manual work.

Been wanting to learn more about the interplay between Deep Learning and Robotics? UC Berkeley Professor Sergey Levine shares his thoughts and recent advances at the CMU Robotics Institute seminar series.

RI Seminar: Sergey Levine : Deep Robotic Learning Sergey Levine Assistant Professor, UC Berkeley April 07, 2017 Abstract Deep learning methods have provided us with remarkably powerful, flexible, and robust ...

To naturally interact with humans robots will need to have grounded language understanding. Igor Mordatch (currently research scientist at OpenAI, formerly bair.berkeley.edu post-doc) latest work showcases the emergence of grounded compositional language in multi-agent populations!

https://openai.com/blog/learning-to-communicate/

Great article on drive.ai's Deep Learning First approach to autonomous driving. drive.ai is led by Berkeley AI Research (bair.berkeley.edu) alumn Sameep Tandon!

Deep Learning First: Drive.ai's Path to Autonomous Driving These cars use deep learning to turn past experience into better decisions

Berkeley a regional center in new robotics manufacturing consortium Berkeley Engineering is a key partner in the new $253 million Advanced Robotics Manufacturing Innovation Hub, launched this month by the Department of Defense to create and deploy next-generation robotic technology.

Professor Ken Goldberg from the Berkeley AI Research lab (bair.berkeley.edu) shares his insights on challenges in robotic manipulation!

Why It's So Hard For Robots To Get A Grip Robots are all thumbs. Engineers are working to improve dexterity so bots can take over housekeeping drudgeworkas well as people's jobs.

Roboticists at UC Berkeley have designed a small robot that can leap into the air and then spring off a wall, or perform multiple vertical jumps in a row, resulting in the highest robotic vertical jumping agility ever recorded.

Video: https://www.youtube.com/watch?v=xvIk39rkkiU

The work will be published Dec. 6 in the debut edition of the journal Science Robotics.

Wall-jumping robot is most vertically agile ever built Roboticists at UC Berkeley have designed a small robot that can leap into the air and then spring off a wall, or perform multiple vertical jumps in a row, resulting in the highest robotic vertical …

How might autonomous cars deal with ambiguous situations? E.g., when two cars arrive at a stop sign at the same time? New work by Dorsa Sadigh, Sanjit Seshia, Shankar Sastry, Anca Drăgan from the Berkeley AI lab shows how autonomous cars can discover solutions to such situations!

Autonomous Cars Could Determine Your Driving Style by Gently Probing You "People might not always react positively to being probed."

Been wanting to learn more about Deep Reinforcement Learning, and how to get? Great opportunity tonight, with former Berkeley student John Schulman is presenting at the SF AI Meet up!

San Francisco Artificial Intelligence Meetup UPDATE: The venue has changed. The new location is the Cafe/Library area at Galvanize on 44 Tehama Street. We are thrilled to announce the John Schulman is joining AI Meetup Series! John is a researc

Announcing Berkeley's Center for Human Compatible AI, headed up by Prof. Stuart Russell!

The goal of CHCAI is to develop the conceptual and technical wherewithal to reorient the general thrust of AI research towards provably beneficial systems.

Artificial intelligence research is concerned with the design of machines capable of intelligent behavior, i.e., behavior likely to be successful in achieving objectives. The long-term outcome of AI research seems likely to include machines that are more capable than humans across a wide range of objectives and environments. This raises a problem of control: given that the solutions developed by such systems are intrinsically unpredictable by humans, it may occur that some such solutions result in negative and perhaps irreversible outcomes for humans. CHCAI's goal is to ensure that this eventuality cannot arise, by refocusing AI away from the capability to achieve arbitrary objectives and towards the ability to generate provably beneficial behavior. Because the meaning of beneficial depends on properties of humans, this task inevitably includes elements from the social sciences in addition to AI.

The Center for Human-Compatible AI is sponsored by the Open Philanthropy Project, the Future of Life Institute, the Leverhulme Trust, and CITRIS. Partner organizations include the Leverhulme Centre for the Future of Intelligence and the Center for Long-Term Security

Center for Human-Compatible AI

Great short video on AI and our future by Prof. Max Welling from the University of Amsterdam!

How will artificial intelligence influence your future?