Startup Kindred brings sliver of hope for AI in robotics ZDNet

Training robots to do simple tasks with so-called deep learning has met with limited success, but a San Francisco startup offers a glimmer of hope for future work. Kindred, a three-year-old startup, Thursday offered up a research paper presented at the 2nd Conference on Robot Learning in Zürich, Switzerland. The thrust of the paper is that roboticists need to establish some basic benchmarks about how machine learning, and particularly deep learning, perform before real-world progress can be made. The paper doesn't prove machine learning can teach a robot to move; rather, it suggests there are ways to systematically identify the challenges to doing so, as a basis for future work. In the report, "Benchmarking Reinforcement Learning Algorithms on Real-World Robots," posted on arXiv on September 20th, the authors, A. Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma, and James Bergstra, took three commercially available robots and had them move in space to a target location.