The RSS Test of Time Award is given to highest impact papers published at RSS (and potentially journal versions thereof) from at least ten years ago. Impact may mean that it changed how we think about problems or about robotic design, that it brought fully new problems to the attention of the community, or that it pioneered new approach to robotic design or problem solving.
With this award, RSS generally wants to foster the discussion of the long term development of our field. The award is an opportunity to reflect on and discuss the past, which is essential to make progress in the future. The awardee’s keynote is therefore complemented with a Test of Time Panel session devoted to this important discussion.
It is our great pleasure to announce that the 2023 Test of Time Award goes to:
Ian Lenz, Honglak Lee, Ashutosh Saxena
Deep Learning for Detecting Robotic Grasps
Robotics: Science and Systems IX, 2013.
Ian Lenz, Honglak Lee, Ashutosh Saxena
Deep Learning for Detecting Robotic Grasps
International Journal of Robotics Research 34(4-5), p. 705-724, 2015
This award has been conferred to this work for contributions to the application of deep learning in robotic manipulation. As one of the earliest applications of deep learning in the domain of robotics, the paper showed that the applications of deep learning extend well beyond the computer vision tasks that were considered at the time. The deep-learning-based methodology applied to multi-modal data typical in robotics, and showcased successful grasps of a wide range of objects for the first time. Various other applications of deep learning in all aspects of robotics, including in robot manipulation, followed in the years after the publication of this paper.