A novel framework for generalizing dynamic movement primitives under kinematic constraints

Sidiropoulos, Antonis; Papageorgiou, Dimitrios; Doulgeri, Zoe

doi:10.1007/s10514-022-10067-4

Cited by 8 publications

(16 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here we compare with the framework from [24] which uses the DMP formulation [23] with the classical DMP scaling to generate the generalized trajectory {y d , ẏd , ÿd } i over a horizon of N future time-steps which is then optimized to respect kinematic limits like position velocity and acceleration bounds as well as passing from via-points. We show that by modifying the framework from [24] according to Fig. 5, where the DMP with the classical scaling is replaced by DMP ++ we achieve more efficient generalization that accounts also Fig.…”

Section: Adding Kinematic Inequality Constraintsmentioning

confidence: 99%

“…Fig. 6: Comparison of the classical DMP spatial generalization against the proposed DMP ++ in combination with [24] to enforce kinematic inequality constraints.…”

Section: Adding Kinematic Inequality Constraintsmentioning

confidence: 99%

“…We achieve this by exploiting the novel DMP formulation presented in our previous work [23] to derive an online adaptation scheme for the DMP weights, based on minimizing the distance from the learned acceleration profile under the equality constraints of via-points. Moreover, we show how the proposed novel generalization can also be combined with our previous work [24] to impose kinematic inequality constraints in order to generate feasible trajectories in the presence of kinematic bounds related to the robot and the task environment (obstacles and via points).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved online DMP spatial generalization and incorporation of dynamic via-points

Sidiropoulos

Doulgeri

2023

Preprint

Self Cite

View full text Add to dashboard Cite

Dynamic Movement Primitives (DMP) have found remarkable applicability and success in various robotic tasks, which can be mainly attributed to their generalization, modulation and robustness properties. Nevertheless, the spatial generalization of DMP can be problematic in some cases, leading to excessive or unnatural spatial scaling. Moreover, incorporating intermediate points (via-points) to adjust the DMP trajectory, is not adequately addressed. In this work we propose an improved online spatial generalization, that remedies the shortcomings of the classical DMP generalization, and moreover allows the incorporation of dynamic via-points. This is achieved by designing an online adaptation scheme for the DMP weights which is proved to minimize the distance from the demonstrated acceleration profile to retain the shape of the demonstration, subject to dynamic via-point and initial/final state constraints. Extensive comparative simulations with the classical and other DMP variants are conducted, while experimental results validate the applicability and efficacy of the proposed method.

show abstract

Section: Adding Kinematic Inequality Constraintsmentioning

confidence: 99%

“…Fig. 6: Comparison of the classical DMP spatial generalization against the proposed DMP ++ in combination with [24] to enforce kinematic inequality constraints.…”

Section: Adding Kinematic Inequality Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved online DMP spatial generalization and incorporation of dynamic via-points

Sidiropoulos

Doulgeri

2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Han et al. [25] proposed an improved DMP that allows the robotic arm to generate trajectories on curved surfaces by adding a scale factor and a force coupling term. Yuan et al.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, some scholars have done lots of studies on the extended use of DMP. Han et al [25] proposed an improved DMP that allows the robotic arm to generate trajectories on curved surfaces by adding a scale factor and a force coupling term. Yuan et al [26] proposed a new hierarchical dynamic movement primitive (HDMP) framework to achieve a smooth robot motion.…”

Section: Adjusted Dmpmentioning

confidence: 99%

Cauchy DMP: Improving 3C industrial assembly quality with the Cauchy kernel and singular value decomposition

Liu,

Zhu,

Luo

et al. 2023

Cognitive Comp and Systems

View full text Add to dashboard Cite

Although Dynamic Movement Primitives (DMP) is an effective tool for robotic arm trajectory generalisation, the application of DMP in the 3C (Computer, Communication, Consumer Electronics) industry still faces challenges such as low precision and high‐time consumption. To address this problem, we propose a novel Cauchy DMP framework. The main improvements and advantages of Cauchy DMP, compared to the original DMP, are (1) since the Cauchy distribution has a simpler model and wider shape, using the Cauchy distribution instead of the Gaussian distribution in the original DMP reduces the complexity of the algorithm and saves time. (2) Singular Value Decomposition (SVD) can effectively model the error. To reduce the interference of the rounding and human error on the trajectory, SVD can be used to obtain the weight of each basis function. The proposed Cauchy DMP framework combines the above two points and is validated on a real UR5 robotic arm. The results show that Cauchy DMP retains the learnability of the original DMP and has the advantages of short time consumption and low error rate.

show abstract

Dynamic Via-points and Improved Spatial Generalization for Online Trajectory Generation with Dynamic Movement Primitives

Sidiropoulos,

Doulgeri

2024

J Intell Robot Syst

View full text Add to dashboard Cite

Dynamic Movement Primitives (DMP) have found remarkable applicability and success in various robotic tasks, which can be mainly attributed to their generalization, modulation and robustness properties. However, the spatial generalization of DMP can be problematic in some cases, leading to excessive overscaling and in turn large velocities and accelerations. While other DMP variants have been proposed in the literature to tackle this issue, they can also exhibit excessive overscaling as we show in this work. Moreover, incorporating intermediate points (via-points) for adjusting the DMP trajectory to account for the geometry of objects related to the task, or to avoid or push aside objects that obstruct a specific task, is not addressed by the current DMP literature. In this work we tackle these unresolved so far issues by proposing an improved online spatial generalization, that remedies the shortcomings of the classical DMP generalization, and moreover allows the incorporation of dynamic via-points. This is achieved by designing an online adaptation scheme for the DMP weights which is proved to minimize the distance from the demonstrated acceleration profile to retain the shape of the demonstration, subject to dynamic via-point and initial/final state constraints. Extensive comparative simulations with the classical and other DMP variants are conducted, while experimental results validate the practical usefulness and efficiency of the proposed method.

show abstract

A novel framework for generalizing dynamic movement primitives under kinematic constraints

Cited by 8 publications

References 25 publications

Improved online DMP spatial generalization and incorporation of dynamic via-points

Improved online DMP spatial generalization and incorporation of dynamic via-points

Cauchy DMP: Improving 3C industrial assembly quality with the Cauchy kernel and singular value decomposition

Dynamic Via-points and Improved Spatial Generalization for Online Trajectory Generation with Dynamic Movement Primitives

Contact Info

Product

Resources

About