M-EMBER: Tackling Long-Horizon Mobile Manipulation via Factorized Domain Transfer

In this paper, we propose a method to create visuomotor mobile manipulation solutions for long-horizon activities. We propose to leverage the recent advances in simulation to train visual solutions for mobile manipulation. While previous works have shown success applying this procedure to autonomous visual navigation and stationary manipulation, applying it to long-horizon visuomotor mobile manipulation is still an open challenge that demands both perceptual and compositional generalization of multiple skills. In this work, we develop Mobile-EMBER, or M-EMBER, a factorized method that decomposes a long-horizon mobile manipulation activity into a repertoire of primitive visual skills, reinforcement-learns each skill, and composes these skills to a long-horizon mobile manipulation activity. On a mobile manipulation robot, we find that M-EMBER completes a long-horizon mobile manipulation activity, cleaning_kitchen, achieving a 53% success rate. This requires successfully planning and executing five factorized, learned visual skills

Dexterous Hexrotor UAV Platform

Mobile manipulation is a hot area of study in robotics as it unites the two classes of robots: locomotors and manipulators. An emerging niche in the field of mobile manipulation is aerial mobile manipulation. Although there has been a fair amount of study of free-flying satellites with graspers, the more recent trend has been to outfit UAVs with graspers to assist various manipulation tasks. While this recent work has yielded impressive results, it is hampered by a lack of appropriate testbeds for aerial mobile manipulation, similar to the state of ground-based mobile manipulation a decade ago. Typical helicopters or quadrotors cannot instantaneously resist or apply an arbitrary force in the plane perpendicular to the rotor axis, which makes them inadequate for complex mobile manipulation tasks. Based on the concept of force closure (a term from the dexterous manipulation community), this thesis introduces the new type of dexterous, 6-DoF UAV which provides the unique capability of being able to resist any applied wrench, or generalized force-torque. In this thesis, we describe the importance of force closure for mobile manipulation, explain why it is lacking in current UAV platforms, and describe how our hexrotor provides this important capability as well as exhibiting holonomic behavior

Humanoid Mobile Manipulation Using Controller Refinement

An important class of mobile manipulation problems are move-to-grasp problems where a mobile robot must navigate to and pick up an object. One of the distinguishing features of this class of tasks is its coarse-to-fine structure. Near the beginning of the task, the robot can only sense the target object coarsely or indirectly and make gross motion toward the object. However, after the robot has located and approached the object, the robot must finely control its grasping contacts using precise visual and haptic feedback. In this paper, it is proposed that move-to-grasp problems are naturally solved by a sequence of controllers that iteratively refines what ultimately becomes the final solution. This paper introduces the notion of a refining sequence of controllers and characterizes this type of solution. The approach is demonstrated in a move-to-grasp task where Robonaut, the NASA/JSC dexterous humanoid, is mounted on a mobile base and navigates to and picks up a geological sample box. In a series of tests, it is shown that a refining sequence of controllers decreases variance in robot configuration relative to the sample box until a successful grasp has been achieved