Robotic cloth manipulation for clothing assistance task using Dynamic Movement Primitives

Joshi, Ravi Prakash; Koganti, Nishanth; Shibata, Tomohiro

doi:10.1145/3132446.3134878

Cited by 18 publications

(14 citation statements)

References 18 publications

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“…This is due to the dependency of the forcing term on the velocity. In fact, the forcing term in (12) deviates the trajectory earlier depending on the module of the velocity when the robot moves in the direction of the obstacle, and not only on the position of the obstacle as in the static gradient formulation (8). We also notice that the shape of the trajectory does not change significantly with different potential models for the obstacles.…”

Section: Experiments With Robots In Simulationmentioning

confidence: 80%

“…The parameters for the dynamic potential function are set as λ = 60, η = 0.2, β = 2 after empirical evaluation. When computing the forcing term for each robot, we compute the velocity term in (12) as the relative velocity between the robots. We test two different straight line trajectories, one with null forcing term and the other with constant speed, to verify the independency of our framework with respect to the specific trajectory to be executed.…”

Section: Experiments With Robots In Simulationmentioning

confidence: 99%

“…They encode the trajectory in a system of second-order linear Ordinary Differential Equation (ODE), where a forcing term is learned as a linear combination of predefined time-dependent functions. They are successfully used in many robotic scenarios, such as cloth manufacturing [12], reproduction of human walk for exoskeletons [8], and collaborative bimanual tasks [4]. Obstacle avoidance for DMPs has been successfully treated for point-like obstacles (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Movement Primitives: Volumetric Obstacle Avoidance

Ginesi

Meli

Calanca

et al. 2019

2019 19th International Conference on Advanced Robotics (ICAR)

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Obstacle avoidance for DMPs is still a challenging problem. In our previous work, we proposed a framework for obstacle avoidance based on superquadric potential functions to represent volumes. In this work, we extend our previous work to include the velocity of the trajectory in the definition of the potential. Our formulations guarantee smoother behavior with respect to state-of-the-art point-like methods. Moreover, our new formulation allows to obtain a smoother behavior in proximity of the obstacle than when using a static (i.e. velocity independent) potential. We validate our framework for obstacle avoidance in a simulated multirobot scenario and with different real robots: a pick-and-place task for an industrial manipulator and a surgical robot to show scalability; and navigation with a mobile robot in dynamic environment.

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Section: Experiments With Robots In Simulationmentioning

confidence: 80%

Section: Experiments With Robots In Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Movement Primitives: Volumetric Obstacle Avoidance

Ginesi

Meli

Calanca

et al. 2019

2019 19th International Conference on Advanced Robotics (ICAR)

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show abstract

“…Robots also present an opportunity to help individuals in getting dressed with a variety of garments. Robot-assisted dressing has been demonstrated on several robotic platforms for assisting both able-bodied participants and real people with disabilities [14], [15], [16], [17], [18], [13], [19], [20], [21], [22].…”

Section: A Physically Assistive Roboticsmentioning

confidence: 99%

Assistive VR Gym: Interactions with Real People to Improve Virtual Assistive Robots

Erickson

Kemp

2020

2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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Versatile robotic caregivers could benefit millions of people worldwide, including older adults and people with disabilities. Recent work has explored how robotic caregivers can learn to interact with people through physics simulations, yet transferring what has been learned to real robots remains challenging. Virtual reality (VR) has the potential to help bridge the gap between simulations and the real world. We present Assistive VR Gym (AVR Gym), which enables real people to interact with virtual assistive robots. We also provide evidence that AVR Gym can help researchers improve the performance of simulation-trained assistive robots with real people. Prior to AVR Gym, we trained robot control policies (Original Policies) solely in simulation for four robotic caregiving tasks (robotassisted feeding, drinking, itch scratching, and bed bathing) with two simulated robots (PR2 from Willow Garage and Jaco from Kinova). With AVR Gym, we developed Revised Policies based on insights gained from testing the Original policies with real people. Through a formal study with eight participants in AVR Gym, we found that the Original policies performed poorly, the Revised policies performed significantly better, and that improvements to the biomechanical models used to train the Revised policies resulted in simulated people that better match real participants. Notably, participants significantly disagreed that the Original policies were successful at assistance, but significantly agreed that the Revised policies were successful at assistance. Overall, our results suggest that VR can be used to improve the performance of simulation-trained control policies with real people without putting people at risk, thereby serving as a valuable stepping stone to real robotic assistance.

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“…They encode the trajectory in a system of second-order linear Ordinary Differential Equation (ODE), where a forcing term is learned as a linear combination of predefined timedependent functions. They are successfully used in many robotic scenarios, such as cloth manufacturing [16], reproduction of human walk for exoskeletons [12], and collaborative bimanual tasks [6]. Obstacle avoidance for DMPs has been successfully treated for point-like obstacles (e.g.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions

Ginesi

Meli

Roberti

et al. 2021

J Intell Robot Syst

View full text Add to dashboard Cite

Obstacle avoidance for Dynamic Movement Primitives (DMPs) is still a challenging problem. In our previous work, we proposed a framework for obstacle avoidance based on superquadric potential functions to represent volumes. In this work, we extend our previous work to include the velocity of the system in the definition of the potential. Our formulations guarantee smoother behavior with respect to state-of-the-art point-like methods. Moreover, our new formulation allows obtaining a smoother behavior in proximity of the obstacle than when using a static (i.e. velocity independent) potential. We validate our framework for obstacle avoidance in a simulated multi-robot scenario and with different real robots: a pick-and-place task for an industrial manipulator and a surgical robot to show scalability; and navigation with a mobile robot in a dynamic environment.

show abstract

Robotic cloth manipulation for clothing assistance task using Dynamic Movement Primitives

Cited by 18 publications

References 18 publications

Dynamic Movement Primitives: Volumetric Obstacle Avoidance

Dynamic Movement Primitives: Volumetric Obstacle Avoidance

Assistive VR Gym: Interactions with Real People to Improve Virtual Assistive Robots

Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions

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