Advances in robotic skill acquisition have made it possible to build general-purpose libraries of primitive skills for downstream manipulation tasks. However, naively executing these learned primitives one after the other is unlikely to succeed without accounting for dependencies between actions prevalent in long-horizon plans. We present Task-Agnostic Policy Sequencing (TAPS), a scalable framework for training manipulation primitives and coordinating their geometric dependencies at plan-time to efficiently solve long-horizon tasks never seen by any primitive during training. Based on the notion that Q-functions encode a measure of action feasibility, we formulate motion planning as a maximization problem over the expected success of each individual primitive in the plan, which we estimate by the product of their Q-values. Our experiments indicate that this objective function approximates ground truth plan feasibility and, when used as a planning objective, reduces myopic behavior and thereby promotes task success. We further demonstrate how TAPS can be used for task and motion planning by estimating the geometric feasibility of candidate action sequences provided by a task planner. We evaluate our approach in simulation and on a real robot.

提出了一种可扩展的框架STAP，可在规划时间内协调技能几何依赖，以解决训练期间任何技能都未见过的长视野任务，通过最大化既包括在计划中的所有技能的联合成功，该优化问题近似于基础真相计划的可行性，并减少了目光短浅的行为

STAP: 无序列任务策略