One of the key advantages of legged robots like humanoids should be how effectively they can move across a wide variety of terrain types to accomplish their task. But Light-Loco-Parkour from the team at Light Origins aims to change that: using only onboard sensing, they show a policy which can decide when to walk, vault, climb, or otherwise traverse as it moves through a complex environment. Unlike many others, it uses sparse seeds instead of relying on a large motion corpus, learning when to use its skills to move around without specific sub-task labels. @ChemXiaodao and @Yuntao144 join us to go into the details. Watch Episode 104 of RoboPapers now, with @micoolcho and @chris_j_paxton, to learn more!