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AI Improves Robotic Performance in DARPA's Machine Common Sense Program by Staff Writers Washington DC (SPX) Jun 23, 2022
Researchers with DARPA's Machine Common Sense (MCS) program demonstrated a series of improvements to robotic system performance over the course of multiple experiments. Just as infants must learn from experience, MCS seeks to construct computational models that mimic the core domains of child cognition for objects (intuitive physics), agents (intentional actors), and places (spatial navigation). Using only simulated training, recent MCS experiments demonstrated advancements in systems' abilities - ranging from understanding how to grasp objects and adapting to obstacles, to changing speed/gait for various goals. "These experiments are important milestones that get us closer to building and fielding robust robotic systems with generalized movement capabilities," said Dr. Howard Shrobe, MCS program manager in DARPA's Information Innovation Office. "The prototype systems don't need large sensor suites to deal with unexpected situations likely to occur in the real world."
Rapidly Adapting to Changing Terrain The algorithm is trained completely in simulation without using any domain knowledge-like reference trajectories or predefined foot trajectory generators and is deployed without any fine-tuning. Real-time terrain adaption is essential for quadruped robots to help military units with load carrying and sensing.
Carrying Dynamic Loads
Understanding How to Grasp Objects University of Utah researchers as part of the Oregon State University MCS team developed an active, grasp-learning algorithm that allows robots with multi-fingered hands to dexterously grasp previously unseen objects when trained entirely in simulation. The new approach enabled the robot to grasp with higher than 93% real-world success on novel objects compared to 78% of existing passive learning approaches.
Additional Research MCS researchers from the University of Washington and two teams from the University of Southern California, Information Sciences Institute are currently using a variety of approaches, including hyperbolic learning. This technique learns the commonsense structure of human behavior and physics from large collections of videos to forecast human actions up to 30 seconds in the future. The researchers are also building a scalable, machine-authored, symbolic knowledge base that will provide a higher quality, larger, and more diverse representation of the world.
"By focusing on commonsense, we are creating the possibility for systems to have the flexibility of human learning and the breadth of human knowledge," Shrobe said. "Fusing this knowledge with advanced robotics could result in highly capable, mission-critical systems that humans will want to have as partners."
Baby's kick in the womb may be key to treating disease and training robots Los Angeles CA (SPX) Jun 23, 2022 Does the nervous system come with instructions for how it should connect to the body or must it figure this out during early development? A new model from researchers at the University of Southern California and Lund University in Sweden suggests that spontaneous movements made by a fetus in the womb (including those kicks) are a key step in getting the body's nervous system "wired up." The researchers' model, published in a pair of papers in the Journal of Neurophysiology, suggests that the complex cir ... read more
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