We estimate the state a noisy robot arm and underactuated hand using an Implicit Manifold Particle Filter (MPF) informed by touch sensors. As the robot touches the world, its state space collapses to a contact manifold that we represent implicitly using a signed distance field. This allows us to extend the MPF to higher (six or more) dimensional state spaces. Earlier work (which explicitly represents the contact manifold) only shows the MPF in two or three dimensions. Through a series of experiments, we show that the implicit MPF converges faster and is more accurate than a conventional particle filter during periods of persistent contact. We present three methods of sampling the implicit contact manifold, and compare them in experiments.
Techniques for estimating the configuration of an articulated body from contact sensors are applicable to humans as well as robots. Researchers in the computer graphics community have instrumented objects with contact sensors @cite_18 and used multi-touch displays @cite_21 to reconstruct the configuration of a human hand from contact observations. Both of these approaches generate natural hand configurations by interpolating between data points collected on the same device. Other work has used machine learning to reconstruct the configuration of a human from the ground reaction forces measured by pressure sensors @cite_6 . We hope that our approach is also useful in these problem domains.
@cite_18 @cite_21 @cite_6
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This paper demonstrates a method for simultaneous transfer of positional and force requirements for in-contact tasks from a human instructor to a robotic arm through kinesthetic teaching. This is achieved by a specific use of the sensory configuration, where a force torque sensor is mounted between the tool and the flange of a robotic arm endowed with integrated torque sensors at each joint. The human demonstration is modeled using Dynamic Movement Primitives. Following human demonstration, the robot arm is provided with the capacity to perform sequential in-contact tasks, for example writing on a notepad a previously demonstrated sequence of characters. During the reenactment of the task, the system is not only able to imitate and generalize from demonstrated trajectories, but also from their associated force profiles. In fact, the implemented framework is extended to successfully recover from perturbations of the trajectory during reenactment and to cope with dynamic environments. Whole-body control in unknown environments is challenging: Unforeseen contacts with obstacles can lead to poor tracking performance and potential physical damages of the robot. Hence, a whole-body control approach for future humanoid robots in (partially) unknown environments needs to take contact sensing into account, e.g., by means of artificial skin. However, translating contacts from skin measurements into physically well-understood quantities can be problematic as the exact position and strength of the contact needs to be converted into torques. In this paper, we suggest an alternative approach that directly learns the mapping from both skin and the joint state to torques. We propose to learn such an inverse dynamics models with contacts using a mixture-of-contacts approach that exploits the linear superimposition of contact forces. The learned model can, making use of uncalibrated tactile sensors, accurately predict the torques needed to compensate for the contact. As a result, tracking of trajectories with obstacles and tactile contact can be executed more accurately. We demonstrate on the humanoid robot iCub that our approach improve the tracking error in presence of dynamic contacts. This paper presents a method for generating dynamically feasible, keyframe-interpolating motions for robots undergoing contact, such as in legged locomotion and manipulation. The first stage generates a twice-differentiable interpolating path that obeys kinematic contact constraints up to a user-specified tolerance. The second stage optimizes speeds along the path to minimize time while satisfying dynamic constraints. The method supports velocity, acceleration, and torque constraints, and polyhedral contact friction constraints at an arbitrary number of contact points. The method is numerically stable, and empirical running time is weakly linear in the number of degrees of freedom and polynomial in the time-domain grid resolution. Experiments demonstrate that full-body motions for robots with 100 degrees of freedom and dozens of contact points are calculated in seconds. Tracking the articulated 3D motion of the hand has important applications, for example, in human-computer interaction and teleoperation. We present a novel method that can capture a broad range of articulated hand motions at interactive rates. Our hybrid approach combines, in a voting scheme, a discriminative, part-based pose retrieval method with a generative pose estimation method based on local optimization. Color information from a multi-view RGB camera setup along with a person-specific hand model are used by the generative method to find the pose that best explains the observed images. In parallel, our discriminative pose estimation method uses fingertips detected on depth data to estimate a complete or partial pose of the hand by adopting a part-based pose retrieval strategy. This part-based strategy helps reduce the search space drastically in comparison to a global pose retrieval strategy. Quantitative results show that our method achieves state-of-the-art accuracy on challenging sequences and a near-real time performance of 10 fps on a desktop computer.