Kinesthetic teaching is an approach to providing demonstrations to a robot in Learning from Demonstration whereby a human physically guides a robot to perform a skill. In the common usage of kinesthetic teaching, the robot's trajectory during a demonstration is recorded from start to end. In this paper we consider an alternative, keyframe demonstrations, in which the human provides a sparse set of consecutive keyframes that can be connected to perform the skill. We present a user-study (n = 34) comparing the two approaches and highlighting their complementary nature. The study also tests and shows the potential benefits of iterative and adaptive versions of keyframe demonstrations. Finally, we introduce a hybrid method that combines trajectories and keyframes in a single demonstration.

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We present a sampling-based motion planner that improves the performance of the probabilistically optimal RRT* planning algorithm. Experiments demonstrate that our planner finds a fast initial path and decreases the cost of this path iteratively. We identify and address the limitations of RRT* in high-dimensional configuration spaces. We introduce a sampling bias to facilitate and accelerate cost decrease in these spaces and a simple node-rejection criteria to increase efficiency. Finally, we incorporate an existing bi-directional approach to search which decreases the time to find an initial path. We analyze our planner on a simple 2D navigation problem in detail to show its properties and test it on a difficult 7D manipulation problem to show its effectiveness. Our results consistently demonstrate improved performance over RRT*.

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We present two foot mechanisms that allow relatively large patches of synthetic fibrillar dry adhesives applied inexactly by a climbing robot to perform at levels previously obtained only for small samples in precisely aligned and controlled bench-top tests. The mechanisms are inspired by the structures found in the toes of the gecko. The first mechanism uses ankles with roll and yaw flexures and a compliant structure behind the adhesive material to achieve approximately uniform pressures under nominal loading conditions on flat and curved surfaces. The second design uses a tendon-supported structure to achieve uniform loading and prevent premature peeling failures despite significant misalignment with a flat wall surface. The two designs are demonstrated on Stickybot III, an approximately 1 kg climbing robot, and can be scaled to larger areas and loads by tiling the basic structure.