Pulling open novel doors and drawers with equilibrium point control

Jain, Abhinav; Kemp, Charles C.

doi:10.1109/ichr.2009.5379532

Cited by 44 publications

(36 citation statements)

References 18 publications

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“…This controller uses the force measured by the wrist force-torque sensor of the robot. We refer the reader to the work of Jain and Kemp (2009b) for details about the implementation of equilibrium point control and how it can be used to coordinate the motion of a mobile base and a compliant arm (Jain and Kemp, 2010).…”

Section: Real-time Model Estimation and Manipulator Controlmentioning

confidence: 99%

“…Katz and Brock (2008) enabled a robot to first interact with a planar kinematic object on a table in order to visually learn its kinematic model and to manipulate it. Jain and Kemp (2009b) presented an approach that enabled a robot to estimate the radius and location of the axis for revolute joints that move in a plane parallel to the ground while opening doors and drawers using equilibrium point control. In contrast to our work, both approaches assume planar objects and learn only two-dimensional kinematic models.…”

Section: Related Workmentioning

confidence: 99%

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Approaches to Probabilistic Model Learning for Mobile Manipulation Robots

Sturm¹

2013

Springer Tracts in Advanced Robotics

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Mobile manipulation robots are envisioned to provide many useful services both in domestic environments as well as in the industrial context. Examples include domestic service robots, that implement large parts of the housework, and versatile industrial assistants, that provide automation, transportation, inspection, and monitoring services. The challenge in these applications is that the robots have to function under changing, real-world conditions, be able to deal with considerable amounts of noise and uncertainty, and operate without the supervision of an expert. To meet these challenges, current robotic systems are typically custom-tailored to specific applications in welldefined environments, and therefore cannot deal robustly with changes in the situation. This thesis presents novel learning techniques that enable mobile manipulation robots, i.e., mobile platforms with one or more robotic manipulators, to autonomously adapt to new or changing situations. The developed approaches in this thesis cover the following four topics: (1) learning the robot's kinematic structure and properties using actuation and visual feedback, (2) learning about articulated objects in the environment in which the robot is operating, (3) using tactile feedback to augment the visual perception, and (4) learning novel manipulation tasks from human demonstrations.In the first part of this thesis, we present innovative approaches to learning a robot's own body schema from scratch using visual self-observation. This allows manipulation robots to calibrate themselves automatically and to adapt their body schemata autonomously, for example after hardware failures or during tool use. In the second part, we extend the developed framework to learning about articulated objects -such as doors and drawers -with which service robots often need to interact. The presented algorithms enable robots to learn accurate kinematic models of articulated objects, which in turn allow them to interact with the objects robustly. In the third part, we provide approaches that allow manipulation robots to make use of tactile perception -an ability that is known to play an important role in human object manipulation skills. The main contributions in this part are approaches to identifying objects and to perceiving aspects of their internal states. With this, a manipulation robot can verify that it has grasped the correct object and, for example, discriminate full from empty bottles. Finally, we present an integrated system that allows human operators to intuitively teach a robot novel manipulation tasks by demonstration.All techniques developed in the thesis are based on probabilistic learning and inference. They have been implemented and evaluated on real robots as well as in simulation. Extensive experiments have been conducted to analyze and validate the properties of the developed algorithms and to demonstrate a significant increase in robustness, adaptability, and utility of mobile manipulation robots in everyday life. Zusammenfassung

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Section: Real-time Model Estimation and Manipulator Controlmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Approaches to Probabilistic Model Learning for Mobile Manipulation Robots

Sturm¹

2013

Springer Tracts in Advanced Robotics

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“…On Cody, we use joint-space impedance control running at 1 kHz (Jain and Kemp 2009b). Although the two robots have different lower-level control and actuation, we use equilibrium point control as described in on both robots to autonomously open doors with a hook end effector.…”

Section: The Robotsmentioning

confidence: 99%

Improving robot manipulation with data-driven object-centric models of everyday forces

Jain

Kemp

2013

Auton Robot

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Based on a lifetime of experience, people anticipate the forces associated with performing a manipulation task. In contrast, most robots lack common sense about the forces involved in everyday manipulation tasks. In this paper, we present data-driven methods to inform robots about the forces that they are likely to encounter when performing specific tasks. In the context of door opening, we demonstrate that data-driven object-centric models can be used to haptically recognize specific doors, haptically recognize classes of door (e.g., refrigerator vs. kitchen cabinet), and haptically detect anomalous forces while opening a door, even when opening a specific door for the first time. We also demonstrate that two distinct robots can use forces captured from people opening doors to better detect anomalous forces. These results illustrate the potential for robots to use shared databases of forces to better manipulate the world and attain common sense about everyday forces.

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“…An optimization algorithm using position of the end-effector has also been used in Kemp (2009, 2010); the algorithm obtains estimates of the radius and the center of the door trajectory and subsequently estimates of the control directions. The velocity reference is composed by the estimated tangential velocity and force feedback (bang-bang control in Jain and Kemp (2009) and proportional control in Jain and Kemp (2010)) along the radial direction and feeds a low level control law to enable a viscoelastic behavior of the system around an equilibrium position. Prats et al (2008) consider an inverse Jacobian velocity control law with feedback of the force error following the Task Space Formalism (Bruyninckx and De Schutter, 1996).The estimation is based on the endeffector trajectory to align the task frame with the vector which is tangent on the hand's trajectory.…”

Section: Related Workmentioning

confidence: 99%