AI DARPA’s Machine Common Sense Program

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
In one experiment, researchers at the University of California, Berkeley developed a rapid motor adaption (RMA) algorithm that allows quadruped robots to adapt rapidly to changing terrain. Using the RMA algorithm and proprioceptive feedback (the sense of self-movement and body position), the robots successfully navigated through a range of both real-world and simulated 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.

Source from DARPA