Willow Garage

About: Willow Garage is a based out in . It is known for research contribution in the topics: Robot & Mobile robot. The organization has 76 authors who have published 191 publications receiving 28617 citations.

A constrained optimization framework for compliant underactuated grasping

Abstract: . This study focuses on the design and analysis of underactuated robotic hands that use tendons and compliant joints to enable passive mechanical adaptation during grasping tasks. We use a quasistatic equilibrium formulation to predict the stability of a given grasp. This method is then used as the inner loop of an optimization algorithm that can find a set of actuation mechanism parameters that optimize the stability measure for an entire set of grasps. We discuss two possible approaches to design optimization using this framework, one using exhaustive search over the parameter space, and the other using a simplified gripper construction to cast the problem to a form that is directly solvable using well-established optimization methods. Computations are performed in 3-D, allow arbitrary geometry of the grasped objects and take into account frictional constraints. This paper was presented at the IFToMM/ASME International Workshop on Underactuated Grasping (UG2010), 19 August 2010, Montreal, Canada.

Perspectives on Agency Interacting with and through Personal Robots

Abstract: Personal robots present opportunities for understanding the ways that people perceive agency-both in-the-moment and reflectively. Autonomous and interactive personal robots allow us to explore how people come to perceive agency of non-human agents. Remote presence and tele-operation systems are expanding our understandings of how people interact through robots, incorporating these systems into their own sense of agency. As such, robotics can inform our understanding of both robotic agency and human agency.

Motion planning with constraints using configuration space approximations

Abstract: Robots executing practical tasks in real environments are often subject to multiple constraints. These constraints include orientation constraints: e.g., keeping a glass of water upright, torque constraints: e.g., not exceeding the torque limits for an arm lifting heavy objects, visibility constraints: e.g., keeping an object in view while moving a robot arm, etc. Rejection sampling, Jacobian projection techniques and optimization-based approaches are just some of the methods that have been used to address such constraints while computing motion plans for robots performing manipulation tasks. In this work, we present an approach to handling certain types of constraints in a manner that significantly increases the efficiency of existing methods. Our approach focuses on the sampling step of a motion planner. We implement this step as the drawing of samples from a set that has been computed in advance instead of the direct sampling of constraints. We show how our approach can be applied to different constraints: orientation constraints on the end-effector of an arm, visibility constraints and dual-arm constraints. We present simulated results to validate our method, comparing it to approaches that use direct sampling of constraints.

Using Near-Field Stereo Vision for Robotic Grasping in Cluttered Environments

Abstract: Robotic grasping in unstructured environments requires the ability to adjust and recover when a pre-planned grasp faces imminent failure. Even for a single object, modeling uncertainties due to occluded surfaces, sensor noise and calibration errors can cause grasp failure; cluttered environments exacerbate the problem. In this work, we propose a simple but robust approach to both pre-touch grasp adjustment and grasp planning for unknown objects in clutter, using a small-baseline stereo camera attached to the gripper of the robot. By employing a 3D sensor from the perspective of the gripper we gain information about the object and nearby obstacles immediately prior to grasping that is not available during head-sensor-based grasp planning. We use a feature-based cost function on local 3D data to evaluate the feasibility of a proposed grasp. In cases where only minor adjustments are needed, our algorithm uses gradient descent on a cost function based on local features to find optimal grasps near the original grasp. In cases where no suitable grasp is found, the robot can search for a significantly different grasp pose rather than blindly attempting a doomed grasp. We present experimental results to validate our approach by grasping a wide range of unknown objects in cluttered scenes. Our results show that reactive pre-touch adjustment can correct for a fair amount of uncertainty in the measured position and shape of the objects, or the presence of nearby obstacles.

Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control

Abstract: The Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control, a fleet of quadrotor helicopters, has been developed as a testbed for novel algorithms that enable autonomous operation of aerial vehicles. The testbed has been used to validate multiple algorithms such as reactive collision avoidance, collision avoidance through Nash Bargaining, path planning, cooperative search and aggressive maneuvering. This article briefly describes the algorithms presented and provides references for a more in-depth formulation, and the accompanying movie shows the demonstration of the algorithms on the testbed.