Physical Twins: Accelerating and Enabling Robot Learning with Phantom Platforms
Authors: Elizabeth Peiros, Derek Chen, Lucas Yager, Edward Lee, Kevin Wang, Zoe Samuels, Michael C. Yip
Organizations: Electrical and Computer Engineering Department, University of California, San Diego, La Jolla, CA 92093 USA · Mechanical and Aerospace Engineering Department, University of California, San Diego, La Jolla, CA 92093 USA
Improvements in human-robot physical interaction (pHRI) can have major implications for physical therapy, search and rescue, and telemedicine. However, a major challenge concerns human constraints and safety in human-robot physical experiments. Concerns about human studies also include repeatability, scalability, and participant diversity. To conduct such experiments, an IRB and willing human participants are required. In this work, we present an improved phantom device, a physical twin, that enables real-world RL-type testing for physically interactive algorithms. The new device not only replicates the ball-and-socket motion of the shoulder but also renders scapular and protraction/retraction motions. The experiments showcase the device's ability to render multiple 3D joint limits and demonstrate a Franka Panda arm sensing limits and interacting with the device as an arm for ADLs (activities of daily living) and pHRI tasks.
Figures & tables
Figure 2: Top image, Fig (a), highlights the rotation of the clavicle bone when x-rayed. The labeling shows both the angular change and the translational motion of the shoulder socket. Bottom image, Fig (b), shows the translation of joint motion into a mechanical mechanism. From top to bottom the acromioclavicular joint is transformed into a pivot joint, the shoulder socket is transformed into a spherical manipulator, and the scapular motion is re-imagined as a 4-bar mechanism.
Figure 3: (a) Model of the haptic shoulder with each components range of motion demonstrated in yellow, pink, and cyan (b) The Spherical Parallel Manipulator (SPM) Mechanism separated out (c) The concentric shaft coupling of the SPM with linked linkages demonstrated with the same color (d) The capstan drive mechanism for the SPM with steel string highlighted in yellow (e) The four-bar and pivot mechanisms separated out (f) Individual components of the four-bar mechanics with yellow being the driving motor, green and blue being the linkages, and red being the PTFE slides (g) Components of the pivot with the motor being highlighted in yellow
Joint Movement
Literature Limit
Our Design
GH elevation
90∘
94∘
GH internal/external rotation
160∘
400∘
Scapular protraction/retraction
40∘
140∘
Scapular elevation
60∘
114∘
Vertical scapular translation
6.24 cm
10.75 cm
Table I: Comparison of Maximum Shoulder Joint Range of Motion
Parameter
Symbol
Value
Units
Noise Floor
NF
0.035
N
Joint Count
Njoints
7
–
SNR Factor
SNRfactor
[5]
–
Total Length
H+U
[0.57]
m
Min Input Torque
taua,min
[0.698]
Nm
Table II: System parameters for the Franka Panda force detection
Figure 4: (a) Frames of the haptic shoulder, frame C is shown offset along the zb axis of B for visual clarity as the origins of B and C are coincident. (b) Diagram of the 4-bar mechanism with lengths, l1:4 , and joints at points p1:4 and the A and B frames. (c) Agile eye with u^ , v^ , and w^ vectors shown with arms 2 and 3 labeled. The table shows the mechanical constants (lengths) of SPM and 4-bar
Figure 5: The input to output torque ratio for the SPM across the configuration space is shown for a tendon resistance torque of 1 Nm pulling the joint back to center. The top image shows the maximum output torque given a specific configuration. The configuration limits an individual motor’s torque output in the desired direction as described in the Jacobian mapping.
Cone Half-Angle
Stiffness k (Nm/rad)
RMS ∣τxy∣ Error (Nm)
30∘
0.50
0.065
1.00
0.086
1.50
0.078
20∘
0.50
0.086
1.00
0.066
1.50
0.019
Table III: Error of rendered torque for 9 haptic cones
Figure 6: The results of experiments with the Franka Panda arm and ATI Axia80 M8 transducer force-torque sensor. Sub-figure (a) shows the wrench torque perceived by the Panda robot with 4 different shoulder ranges of motion. At the lowest range of motion, the robot perceives the highest resistance forces, emulating the stretching of tendons visualized with a bright pink. The largest range in which the robot does not hit the end ranges during its motion is nearly all shown in blue, indicating very low perceived tendon forces. Sub-figure (b) validates the Jacobian transform between the motor torque and output torque of the device. The ATI Axia80 M8 was used to measure the Haptic wrench (resistance torque), which was compared to the desired input torque direction and magnitude. Sub-figure (c) shows the attachment of the robot to the shoulder with the load cell.
Figure 7: Still captures from experiments involving the use of the Franka Panda platform performing tasks. Series (a) depicts the robot grasping the upper arm and lifting the arm from a relaxed position to a position within an acceptable range for [ 7 ] . The second series (b) shows the arm using WAM to lift the device from the armpit, which can be used to understand motions for patient transfer tasks.