Learning at your fingertips

Par

Vikash Kumar

Google AI

Résumé:

While the last decade saw incredible progress in the field of robotic locomotion, with robots increasingly capable of navigating different terrains both indoors and outdoors, current robotic manipulation abilities are not at par with tasks that await at their destination. Most common robots have limited dexterity which restricts their manipulation abilities to simple pick and place operations. Useful tasks found in common homes, shops, hospitals, etc. are far too complex, and rarely involve picking up objects (TV remotes, wallets, scissors, keys, etc.) to simply place it down without using! We need to endow our robots with enhanced dexterity and the ability to exhibit a wide range of manipulation skills to meet the challenges presented by human-centric environments. In this talk, I will outline my efforts towards imparting human-level dexterity to our robots in a scalable way. Dexterous manipulation involves solving for fine motor behaviors that leverage intermittent contact events to delicately balance the object under manipulation. Acquiring such intricate behaviors in the real world while ensuring the stability of the object has proven to be notoriously hard to solve using existing robotics methods. Recently, data-driven techniques have been quite successful in generating motor-skills in simulations and simpler systems. However, these techniques in their current form are less effective in contact-rich dexterous manipulation problems, especially on real robots. This talk will draw insight from the fields of robotics, optimal control, machine learning, and game theory to design algorithms that deliver a new state of the art in standard robotics benchmark problems. On real systems, the proposed techniques scale gracefully to high-dimensional, contact-rich problems, and learns various dexterous manipulation behaviors directly via real-world interactions providing a significant boost to robotic capability towards human-level dexterity.

Biographie :

Vikash Kumar finished his Ph.D. from the University of Washington with Prof. Emo Todorov and Prof. Sergey Levine, where his research focused on imparting human-level dexterity to anthropomorphic robotic hands. He continued his research as a post-doctoral fellow with Prof. Sergey Levine at Univ. of California Berkeley where he further developed his methods to work on low-cost scalable systems. He also spent time as a Research Scientist at OpenAI and Google-Brain where he diversified his research on low-cost scalable systems to the domain of multi-agent locomotion. He has also been involved with the development of the MuJoCo physics engine, now widely used in the fields of Robotics and Machine Learning. His works have been recognized with the best manipulation paper at ICRA’16, the biggest robotics conference, and have been widely covered with a wide variety of media outlets such as NewYorkTimes, Reuters, ACM, WIRED, MIT Tech reviews, IEEE Spectrum, etc.