Interaction Control of Collaborative Robots for Adaptation ... · −The impedance/force parameters are usually pre-defined by programmers −This will limit the interaction capacity

Interaction Control of Collaborative Robots for Adaptation to Task and Human Factors

Arash Ajoudani

Fondazione Istituto Italiano di Tecnologia [email protected]

HRI2 Research

Assistive Robotics / Power augmentation

Physical Human-Robot & Robot-Robot Interaction/Cooperation

Teleoperation/Teleimpedance Human Motor Control Understanding for

Robotic Manipulation


Robots with advanced physical interaction capabilities: Integration to human environments

Laghi et al., IJRR 2018

Peternel et al., RAL 2016

Ajoudani et al., Humanoids 2014


Robots, Humans, and the Environment

H

R

E



H

R

E


Robot interaction with the environment

− How to tune robot control parameters (i.e., impedance/force gains)?

− The impedance/force parameters are usually pre-defined by

programmers

− This will limit the interaction capacity of the robots!


Approach 1: Replicating human physical interaction behavior

S. Fani et al., IEEE RAM 2018

A. Ajoudani et al., IJRR 2017

x 1.5



Adaptation to external disturbances

A. Scibilia et al., Humanoids 2018


Approach 2:Robots with interaction autonomy

− Self-tuning of the impedance gains/interaction force based on the

robot sensory feedback

− How to distinguish good interaction from external disturbances?

− What sensory systems are required to achieve robot interaction

autonomy?

See Pietro’s presentation on Thursday at 9:00 in Room 2.L2 (Control I)


Towards robot interaction autonomy:Explore, Identify, and Interact

Kawato, Mitsuo. "Internal models for motor control and trajectory planning." Current opinion in neurobiology 9.6 (1999): 718-727.


Towards robot interaction autonomy:Explore, Identify, and Interact

P. Balatti et al., RAL’19, under review



H

R

E


Human Robot Collaboration: SOA

F. Flacco et al., ICRA 2012


Can CoBots physically assist humans instead of avoiding them?


Physical Overloading Reduction


Physical Overloading Reduction


Dynamics of interaction

𝑧𝑥

𝑪𝑃𝚺𝑾

𝚺𝟎

𝑪𝑃𝒇𝑤𝑜

𝒇𝑤𝑡

𝒇ℎ∆𝜏

Without interaction forcesWith interaction forces

∆𝜏

∆𝜏

W. Kim et al., RAL 2017


Ergonomic Human Robot Collaboration

𝑧𝑥

𝑪𝑃𝚺𝑾

𝚺𝟎

Offline OnlineOffline

WarningShoulder

Hip

Knee∆𝜏 ↓

Moving to reduce

the overloading

joint torque

𝜹𝒑

𝑪𝑃 𝑪𝑃𝒇𝑤𝑜 𝒇𝑤𝑡

𝒇ℎ∆𝜏

Without

interaction

forces

With

interaction

forces

∆𝜏

∆𝜏


SESC: Human CoM Model

𝑧𝑥

𝑪𝑃𝚺𝑾

𝚺𝟎

OfflineOffline

Human CoP model using statically equivalent serial chain (SESC)

S. Cotton, et al., ASME (2009).

Identification the body parameters

p : number of posture

See Wansoo’s presentation on Thursday at 18:00 in Room 2. R1 (Haptics)


Overloading Joint Torque

CO

Px

RAL 2017, ICORR 2017


Human-Robot Load-Sharing

W. Kim, J. Lee, L. Peternel, N. Tsagarakis and A. Ajoudani, "Anticipatory Robot Assistance for the Prevention of Human Static Joint Overloading in Human-RobotCollaboration", IEEE Robotics and Automation Letters (RA-L), 2017.


Ergonomic Human-Robot Co-Manipulation

Modified SESC Model

Dynamic CoP


Ergonomic Human-Robot Co-Manipulation

L. Peternel et al., Humanoids 2017

W. Kim et al., IEEE Transactions on man machine systems , under review


Ergonomic Handover


Human-Robot Handover

L. Peternel et al., Humanoids 2017 W. Kim et al., IEEE Transactions on man machine systems , under review


Replacing wearable suit with vision

Detection of human in 2D image and then reconstructing 3D information using disparity map by applying with some biophysical constraints (OpenPose model)


First Ergonomic and reconfigurable HRC Framework KUKA Innovation Challange 2018

Wansoo Kim

Marta Lorenzini

Pietro Balatti

Phuong Nguyen

x 1.5


KUKA Innovation Winner

Prize: 20K Euros


Physical Fatigue Management

IEEE Transactions on Neural Systems & Rehabilitation, 2017 / RAL 2017 / IROS’16


Physical Fatigue Management

Humanoids 2016 (best paper award finalist) / Autonomous Robots, 2017

See Luka’s presentation on Tuesday at 14:30 in Room 1.L3 (HRI II)


Conclusion

−The principles behind human physical interaction can help us achieve similar performances in robots.

−Use of sensory systems to analyse human factors during interaction.

−Adaptation to humans does not necessarily mean avoiding them (coexistence vs collaboration)

−Collaborative robots can do more than repetitive tasks!


HRI2 paper presentations at IROS

Control of grasp stiffness through CMS and CDS control- See Virginia ‘s presentation on Wednesday at 12:30 in Room 2.R1 (Grasping III)

Ergonomic Feedback Interfaces for Human-Robot Collaboration- See Wansoo’s presentation on Thursday at 9:00 in Room 2.L2 (Control I)

Human fatigue modeling in Human-Robot Collaboration- See Luka’s presentation Tuesday at 14:30 in Room 1.L3 (HRI II)

Auto-tuning Impedance control- See Pietro’s presentation on Thursday at 9:00 in Room 2.L2 (Control I)

Robot Fatigue modeling and management- See Luka’s presentation on Wednesday at 9:0 in Room 1.L3 (HRI V)

Supernumerary hand for daily/industrial assistance- See Andrea’s presentation on Tuesday, Room 2.R1 (Robot Design)


Team HRI2

Adriano Scibilia, MSc studentLeonardo Frezza, MSc student


Chapter 3: Feedback InterfacesVisual

M. Lorenzini et al., WeRob 2018, under review

Wansoo Kim

Marta Lorenzini


Chapter 3: Feedback InterfacesVibro-Tactile

68.1 mm

54.1 mm

37

.0

mm

A A

17

.3

mm

𝑅 = 123 mm

A - A

BackLower limbUpper limb

Motor controllerMotor

Battery

Microcontroller

W. Kim et al., RAL/IROS 2018, under review


Chapter 3: Feedback InterfacesVibro-Tactile

W. Kim et al., RAL/IROS 2018, under review

Documents

Interaction Control of Collaborative Robots for Adaptation ... · −The impedance/force parameters are usually pre-defined by programmers −This will limit the interaction capacity