Search for: All records

We propose a new policy class, Composable Interaction Primitives (CIPs), specialized for learning sustained-contact manipulation skills like opening a drawer, pulling a lever, turning a wheel, or shifting gears. CIPs have two primary design goals: to minimize what must be learned by exploiting structure present in the world and the robot, and to support sequential composition by construction, so that learned skills can be used by a task-level planner. Using an ablation experiment in four simulated manipulation tasks, we show that the structure included in CIPs substantially improves the efficiency of motor skill learning. We then show that CIPs can be used for plan execution in a zero-shot fashion by sequencing learned skills.We validate our approach on real robot hardware by learning and sequencing two manipulation skills. 

Real-world robot task planning is intractable in part due to partial observability. A common approach to reducing complexity is introducing additional structure into the decision process, such as mixed-observability, factored states, or temporally-extended actions. We propose the locally observable Markov decision process, a novel formulation that models task-level planning where uncertainty pertains to object-level attributes and where a robot has subroutines for seeking and accurately observing objects. This models sensors that are range-limited and line-of-sight—objects occluded or outside sensor range are unobserved, but the attributes of objects that fall within sensor view can be resolved via repeated observation. Our model results in a three-stage planning process: first, the robot plans using only observed objects; if that fails, it generates a target object that, if observed, could result in a feasible plan; finally, it attempts to locate and observe the target, replanning after each newly observed object. By combining LOMDPs with off-the-shelf Markov planners, we outperform state-of-the-art solvers for both object-oriented POMDP and MDP analogues with the same task specification. We then apply the formulation to successfully solve a task on a mobile robot.