引入了两个新的基准测试LIBERO-Industry和LADDER，以在更现实和复杂的条件下评估机器人操作能力。LIBERO-Industry侧重于工业流程约束，如不同的产品几何形状和严格的执行窗口，揭示了当前通用机器人策略的性能下降。LADDER解决了长时任务的挑战，表明虽然编排方法比基本的低级策略提高了性能，但由于执行错误和成本预算，它们仍然面临局限性。
AI
arXiv:2610.16782v1 Announce Type: new Abstract: Industrial manipulation in flexible manufacturing, e.g., high-mix, low-volume (HMLV) electronics assembly, requires robot policies to adapt to changing products and production conditions. Such settings impose industrial process cons…
arXiv cs.AI
TIER_1English(EN)·Kaiyuan Chen, Shuangyu Xie, Uksang Yoo, Xinyue Zhu, Cael Magner, Alvin Zhu, Daehwa Kim, Eric Heiden, Erwin Coumans, Miles Macklin, Spencer Huang, Ken Goldberg·
arXiv:2610.18835v2 Announce Type: cross Abstract: It has been difficult to accurately model the physics of the interaction between a robot and the objects it manipulates, including contact mechanics, coupled contact dynamics, deformations, stiffness and friction. We present RoboS…
It is difficult to accurately model the physics of the interaction between a robot and the objects it manipulates, including contact mechanics, coupled contact dynamics, deformations, stiffness and friction. We present RoboSimStudio (RSS), an interactive toolkit that makes choice…
General-purpose robot manipulation policies learn short-horizon skills effectively, but degrade rapidly on long-horizon tasks. Existing benchmarks cannot isolate failures because task horizon is either too short or entangled with scene and object variations. We propose LADDER, a …
