Simultaneous kinesthetic teaching of positional and force requirements for sequential in-contact tasks

Steinmetz, Franz; Montebelli, Alberto; Kyrki, Ville

doi:10.1109/humanoids.2015.7363552

Cited by 44 publications

(27 citation statements)

References 17 publications

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“…Steinmetz et al [8] handle the case of ensuring contact when an expected contact is not satisfied, but require switching between multiple controllers, which is known to suffer stability issues [28]. Additionally, [8] cannot adapt to contacts made sooner than expected. Our extensions to the DMP framework enable robust transitions from free-space motion to constrained motion using a single unified controller.…”

Section: B Force Feedback For Dynamic Movement Primitivesmentioning

confidence: 99%

Learning Task Constraints from Demonstration for Hybrid Force/Position Control

Conkey

Hermans

2019

2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids)

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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.

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Section: B Force Feedback For Dynamic Movement Primitivesmentioning

confidence: 99%

Learning Task Constraints from Demonstration for Hybrid Force/Position Control

Conkey

Hermans

2019

2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids)

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“…Most of the methods of learning human actions operate at the level of configuration-space trajectories [8] [9] [10]. Some researchers used kinesthetic teaching as the approach to provide demonstrations for a robot to learn from demonstrations [2] [11] [12]. However, it is difficult to collect suitable demonstrations for real-world robotic manipulation via kinesthetic teaching.…”

Section: Related Workmentioning

confidence: 99%

Learning Actions from Human Demonstration Video for Robotic Manipulation

Yang

Zhang

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Learning actions from human demonstration is an emerging trend for designing intelligent robotic systems, which can be referred as video to command. The performance of such approach highly relies on the quality of video captioning. However, the general video captioning methods focus more on the understanding of the full frame, lacking of consideration on the specific object of interests in robotic manipulations. We propose a novel deep model to learn actions from human demonstration video for robotic manipulation. It consists of two deep networks, grasp detection network (GNet) and video captioning network (CNet). GNet performs two functions: providing grasp solutions and extracting the local features for the object of interests in robotic manipulation. CNet outputs the captioning results by fusing the features of both full frames and local objects. Experimental results on UR5 robotic arm show that our method could produce more accurate command from video demonstration than state-of-the-art work, thereby leading to more robust grasping performance.

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“…These were applied in a feed-forward manner in control. Similarly, joint torques along the kinematic trajectory were learned and encoded as DMPs in [15] and used to increase the accuracy in the next execution of the incontact task. This approach to improving trajectory execution was also applied to full-sized humanoid robots, for example in [16], where a particular trajectory was run, and the joint torques from that trial were used as the feedforward term on the next trial.…”

Section: Related Workmentioning

confidence: 99%

Learning Task-Specific Dynamics to Improve Whole-Body Control

Gams¹,

Mason²,

Ude³

et al. 2018

2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids)

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In task-based inverse dynamics control, reference accelerations used to follow a desired plan can be broken down into feedforward and feedback trajectories. The feedback term accounts for tracking errors that are caused from inaccurate dynamic models or external disturbances. On underactuated, free-floating robots, such as humanoids, high feedback terms can be used to improve tracking accuracy; however, this can lead to very stiff behavior or poor tracking accuracy due to limited control bandwidth. In this paper, we show how to reduce the required contribution of the feedback controller by incorporating learned task-space reference accelerations. Thus, we i) improve the execution of the given specific task, and ii) offer the means to reduce feedback gains, providing for greater compliance of the system. With a systematic approach we also reduce heuristic tuning of the model parameters and feedback gains, often present in real-world experiments. In contrast to learning task-specific joint-torques, which might produce a similar effect but can lead to poor generalization, our approach directly learns the task-space dynamics of the center of mass of a humanoid robot. Simulated and real-world results on the lower part of the Sarcos Hermes humanoid robot demonstrate the applicability of the approach.

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Simultaneous kinesthetic teaching of positional and force requirements for sequential in-contact tasks

Cited by 44 publications

References 17 publications

Learning Task Constraints from Demonstration for Hybrid Force/Position Control

Learning Task Constraints from Demonstration for Hybrid Force/Position Control

Learning Actions from Human Demonstration Video for Robotic Manipulation

Learning Task-Specific Dynamics to Improve Whole-Body Control

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