We demonstrate that moving the wrist while the fingers perform a grasp increases performance. The coordination shapes the interactions between the fingers, the object and its environment to extend the hand capabilities (e.g. higher payload and precision). We evaluated our hypothesis with a human grasping study where the volunteers grasped objects by moving the soft RBO Hand 2 while its fingers closed in a predefined motion. We limited their ability to coordinate their motion with the finger movements using a compliant robot attached to the hand, and observed that their grasp success decreases with increased constraints. We also successfully transferred one of the observed movement patterns to the robot, indicating that adaptive intrinsic/extrinsic motion increases robotic grasp performance as well.

In this paper we present an approach for creating complete shape representations from a single depth image for robot grasping. We introduce algorithms for completing partial point clouds based on the analysis of symmetry and extrusion patterns in observed shapes. Identified patterns are used to generate a complete mesh of the object, which is, in turn, used for grasp planning. The approach allows robots to predict the shape of objects and include invisible regions into the grasp planning step. We show that the identification of shape patterns, such as extrusions, can be used for fast generation and optimization of grasps. Finally, we present experiments performed with our humanoid robot executing pick-up tasks based on single depth images and discuss the applications and shortcomings of our approach.

Mobile manipulators and humanoid robots should have the ability to use objects in their environments. Previous work has shown significant advantages to robots that can remove objects that interfere with their goal. We propose the next step. Just like the fictional character ‘MacGyver,’ robots should construct simple machines and tools from arbitrary objects. This video presents our progress in developing and validating ‘MacGyver’ skills in a simulated rescue mission.

Robots are inherently limited by constraints on their motor power, battery life, and structural rigidity. Using simple machines and exploiting their mechanical advantage can significantly increase the breadth of a robot's capabilities. In this work, we present an autonomous planner which allows a robot to determine how arbitrary rigid objects in its environment can be utilized in machine designs to overcome physical challenges. First, the designed structure must be sufficient to achieve a task given the input force and torque that can be applied by the robot. Second, the structure must be accessible to the robot given its kinematics and geometry so that it can actually be used to perform the task. The output of our algorithm is the configuration of the design components, the pose of the robot to make contact with the design, and the motor torques needed to actuate it. We demonstrate results with the robot Golem Krang, using levers as simple machines, to overturn 100 kg load and to push 240 kg wheeled obstacle.

On the path to full autonomy, robotic agents have to learn how to manipulate their environments for their benefit. In particular, the ability to design structures that are functional in overcoming challenges is imperative. The problem of automated design of functional structures (ADFS) addresses the question of whether the objects in the environment can be placed in a useful configuration. In this work, we first make the observation that the ADFS problem represents a class of problems in high dimensional, continuous spaces that can be broken down into simpler subproblems with semantically meaningful actions. Next, we propose a framework where discrete actions that induce constraints can partition the solution space effectively. Subsequently, we solve the original class of problems by searching over the available actions, where the evaluation criteria for the search is the feasibility test of the accumulated constraints. We prove that with a sound feasibility test, our algorithm is complete. Additionally, we argue that a convexity requirement on the constraints leads to significant efficiency gains. Finally, we present successful results to the ADFS problem.

The RoboCup robot soccer Small Size League has been running since 1997 with many teams success-fully competing and very effectively playing the games. Teams of five robots, with a combined au-tonomous centralized perception and control, and distributed actuation, move at high speeds in the field space, actuating a golf ball by passing and shooting it to aim at scoring goals. Most teams run their own pre-defined team strategies, unknown to the other teams, with flexible game-state dependent assignment of robot roles and positioning. How-ever, in this fast-paced noisy real robot league, rec-ognizing the opponent team strategies and accord-ingly adapting one’s own play has proven to be a