Learning Task Constraints from Demonstration for Hybrid Force/Position Control

We present a novel method for learning hybrid force/position control from demonstration. We learn a dynamic constraint frame aligned to the direction of desired force using Cartesian Dynamic Movement Primitives. In contrast to approaches that utilize a fixed constraint frame, our approach easily accommodates tasks with rapidly changing task constraints over time. We activate only one degree of freedom for force control at any given time, ensuring motion is always possible orthogonal to the direction of desired force. Since we utilize demonstrated forces to learn the constraint frame, we are able to compensate for forces not detected by methods that learn only from the demonstrated kinematic motion, such as frictional forces between the end-effector and the contact surface. We additionally propose novel extensions to the Dynamic Movement Primitive (DMP) framework that encourage robust transition from free-space motion to in-contact motion in spite of environment uncertainty. We incorporate force feedback and a dynamically shifting goal to reduce forces applied to the environment and retain stable contact while enabling force control. Our methods exhibit low impact forces on contact and low steady-state tracking error.Comment: Under revie

Learning Task Priorities from Demonstrations

Bimanual operations in humanoids offer the possibility to carry out more than one manipulation task at the same time, which in turn introduces the problem of task prioritization. We address this problem from a learning from demonstration perspective, by extending the Task-Parameterized Gaussian Mixture Model (TP-GMM) to Jacobian and null space structures. The proposed approach is tested on bimanual skills but can be applied in any scenario where the prioritization between potentially conflicting tasks needs to be learned. We evaluate the proposed framework in: two different tasks with humanoids requiring the learning of priorities and a loco-manipulation scenario, showing that the approach can be exploited to learn the prioritization of multiple tasks in parallel.Comment: Accepted for publication at the IEEE Transactions on Robotic

ILoSA: Interactive Learning of Stiffness and Attractors

Teaching robots how to apply forces according to our preferences is still an open challenge that has to be tackled from multiple engineering perspectives. This paper studies how to learn variable impedance policies where both the Cartesian stiffness and the attractor can be learned from human demonstrations and corrections with a user-friendly interface. The presented framework, named ILoSA, uses Gaussian Processes for policy learning, identifying regions of uncertainty and allowing interactive corrections, stiffness modulation and active disturbance rejection. The experimental evaluation of the framework is carried out on a Franka-Emika Panda in three separate cases with unique force interaction properties: 1) pulling a plug wherein a sudden force discontinuity occurs upon successful removal of the plug, 2) pushing a box where a sustained force is required to keep the robot in motion, and 3) wiping a whiteboard in which the force is applied perpendicular to the direction of movement

Merging Position and Orientation Motion Primitives

In this paper, we focus on generating complex robotic trajectories by merging sequential motion primitives. A robotic trajectory is a time series of positions and orientations ending at a desired target. Hence, we first discuss the generation of converging pose trajectories via dynamical systems, providing a rigorous stability analysis. Then, we present approaches to merge motion primitives which represent both the position and the orientation part of the motion. Developed approaches preserve the shape of each learned movement and allow for continuous transitions among succeeding motion primitives. Presented methodologies are theoretically described and experimentally evaluated, showing that it is possible to generate a smooth pose trajectory out of multiple motion primitives

Optimizing Programming by Demonstration for in-contact task models by Incremental Learning

Despite the increasing usage of robots for industrial applications, many aspects prevent robots from being used in daily life. One of these aspects is that extensive knowledge in programming a robot is necessary to make the robot achieve a desired task. Conventional robot programming is complex, time consuming and expensive, as every aspect of a task has to be considered. Novel intuitive and easy to use methods to program robots are necessary to facilitate the usage in daily life. This thesis proposes an approach that allows a novice user to program a robot by demonstration and provides assistance to incrementally refine the trained skill. The user utilizes kinesthetic teaching to provide an initial demonstration to the robot. Based on the information extracted from this demonstration the robot starts executing the demonstrated task. The assistance system allows the user to train the robot during the execution and thus refine the model of the task. Experiments with a KUKA LWR4+ industrial robot evaluate the performance of the assistance system and advantages over unassisted approaches. Furthermore a user study is performed to evaluate the interaction between a novice user and robot

A passivity-based strategy for manual corrections in human-robot coaching

In recent years, new programming techniques have been developed in the human-robot collaboration (HRC) field. For example, walk-through programming allows to program the robot in an easy and intuitive way. In this context, a modification of a portion of the trajectory usually requires the teaching of the path from the beginning. In this paper we propose a passivity-based method to locally change a trajectory based on a manual human correction. At the beginning the robot follows the nominal trajectory, encoded through the Dynamical Movement Primitives, by setting high control gains. When the human grasps the end-effector, the robot is made compliant and he/she can drive it along the correction. The correction is optimally joined to the nominal trajectory, resuming the path tracking. In order to avoid unstable behaviors, the variation of the control gains is performed exploiting energy tanks, preserving the passivity of the interaction. Finally, the correction is spatially fixed so that a variation in the boundary conditions (e.g., the initial/final points) does not affect the modification

Incremental Bootstrapping of Parameterized Motor Skills

Queißer J, Reinhart F, Steil JJ. Incremental Bootstrapping of Parameterized Motor Skills. In: Proc. IEEE Humanoids. IEEE; 2016.Many motor skills have an intrinsic, low-dimensional parameterization, e.g. reaching through a grid to different targets. Repeated policy search for new parameterizations of such a skill is inefficient, because the structure of the skill variability is not exploited. This issue has been previously addressed by learning mappings from task parameters to policy parameters. In this work, we introduce a bootstrapping technique that establishes such parameterized skills incrementally. The approach combines iterative learning with state-of-the-art black-box policy optimization. We investigate the benefits of incrementally learning parameterized skills for efficient policy retrieval and show that the number of required rollouts can be significantly reduced when optimizing policies for novel tasks. The approach is demonstrated for several parameterized motor tasks including upper-body reaching motion generation for the humanoid robot COMAN

Robot Programming by Demonstration: Trajectory Learning Enhanced by sEMG-Based User Hand Stiffness Estimation

Trajectory learning is one of the key components of robot Programming by Demonstration approaches, which in many cases, especially in industrial practice, aim at defining complex manipulation patterns. In order to enhance these methods, which are generally based on a physical interaction between the user and the robot, guided along the desired path, an additional input channel is considered in this article. The hand stiffness, that the operator continuously modulates during the demonstration, is estimated from the forearm surface electromyography and translated into a request for a higher or lower accuracy level. Then, a constrained optimization problem is built (and solved) in the framework of smoothing B-splines to obtain a minimum curvature trajectory approximating, in this manner, the taught path within the precision imposed by the user. Experimental tests in different applicative scenarios, involving both position and orientation, prove the benefits of the proposed approach in terms of the intuitiveness of the programming procedure for the human operator and characteristics of the final motion

Efficient and intuitive teaching of redundant robots in task and configuration space

Emmerich C. Efficient and intuitive teaching of redundant robots in task and configuration space. Bielefeld: Universität Bielefeld; 2016.A major goal of current robotics research is to enable robots to become co-workers that learn from and collaborate with humans efficiently. This is of particular interest for small and medium-sized enterprises where small batch sizes and frequent changes in production needs demand a high flexibility in the manufacturing processes. A commonly adopted approach to accomplish this goal is the utilization of recently developed lightweight, compliant and kinematically redundant robot platforms in combination with state-of-the-art human-robot interfaces. However, the increased complexity of these robots is not well reflected in most interfaces as the work at hand points out. Plain kinesthetic teaching, a typical attempt to enable lay users programming a robot by physically guiding it through a motion demonstration, not only imposes high cognitive load on the tutor, particularly in the presence of strong environmental constraints. It also neglects the possible reuse of (task-independent) constraints on the redundancy resolution as these have to be demonstrated repeatedly or are modeled explicitly reducing the efficiency of these methods when targeted at non-expert users. In contrast, this thesis promotes a different view investigating human-robot interaction schemes not only from the learner’s but also from the tutor’s perspective. A two-staged interaction structure is proposed that enables lay users to transfer their implicit knowledge about task and environmental constraints incrementally and independently of each other to the robot, and to reuse this knowledge by means of assisted programming controllers. In addition, a path planning approach is derived by properly exploiting the knowledge transfer enabling autonomous navigation in a possibly confined workspace without any cameras or other external sensors. All derived concept are implemented and evaluated thoroughly on a system prototype utilizing the 7-DoF KUKA Lightweight Robot IV. Results of a large user study conducted in the context of this thesis attest the staged interaction to reduce the complexity of teaching redundant robots and show that teaching redundancy resolutions is feasible also for non-expert users. Utilizing properly tailored machine learning algorithms the proposed approach is completely data-driven. Hence, despite a required forward kinematic mapping of the manipulator the entire approach is model-free allowing to implement the derived concepts on a variety of currently available robot platforms

Robot Programming by Demonstration: Trajectory Learning Enhanced by sEMG-Based User Hand Stiffness Estimation

Trajectory learning is one of the key components of robot Programming by Demonstration approaches, which in many cases, especially in industrial practice, aim at defining complex manipulation patterns. In order to enhance these methods, which are generally based on a physical interaction between the user and the robot, guided along the desired path, an additional input channel is considered in this article. The hand stiffness, that the operator continuously modulates during the demonstration, is estimated from the forearm surface electromyography and translated into a request for a higher or lower accuracy level. Then, a constrained optimization problem is built (and solved) in the framework of smoothing B-splines to obtain a minimum curvature trajectory approximating, in this manner, the taught path within the precision imposed by the user. Experimental tests in different applicative scenarios, involving both position and orientation, prove the benefits of the proposed approach in terms of the intuitiveness of the programming procedure for the human operator and characteristics of the final motion