Author: Ariel Kapusta

A Benchmark Set of Domestic Objects for Service Robot Manipulation

The following everyday, household objects are ranked based on interviews with 25 ALS patients from the Emory ALS Center. We asked the participants to provide relative importance of retrieval of each object by a robot. Although the objects list was validated by population of ALS patients and focused on object retrieval, we hope the object lists provide a common set of practical objects for evaluating wider manipulation and grasp strategies.

The object list in this web page is based on the latest data collected until February 2009. For previous object lists, please refer following resources:

 

rank image orientation name procurement
1 –
TV Remote
 Amazon.com
2 –
Medicine Pill
 See #4 (Medicine Bottle)
3 –
Cordless Phone
 Amazon.com
4 –
Medicine Bottle
 Amazon.com
5 –
Fork
 PlasticMetal, Amazon.com
6 –
Glasses
 Amazon.com
7 –
Toothbrush
 Local Convenience Store
8 –
Spoon
 See #5 (fork)
9 –
Cell Phone
 Amazon.com
10 –
Toothpaste
 Local Convenient Store
11 –
Book
 Amazon.com
12 –
Hand Towel
 Amazon.com
13 –
Mail
 Amazon.com
14
–
Cup / Mug
 PlasticCeramic, Amazon.com
15 –
Soap
 Local Convenient Store
16 –
Disposable Bottle
 Amazon.com
17 –
Shoe / Sandal
 Amazon.com
18 –
Bowl
 Amazon.com
19 –
Keys
 Amazon.com
20 –
Dish Plate
 Amazon.com
21 –
Pen / Pencil
 Amazon.com
22 –
Table Knife
 See #5 (fork)
23 –
Credit Card
 Local Convenience Store
24 –
Medicine Box
 Amazon.com
25 –
Bill
 N/A
26 –
Straw
 Amazon.com
27 –
Magazine
 Local Convenience Store
28 –
Plastic Container
 Amazon.com
29 –
Newspaper
 Local Convenience Store
30 –
Non-Disposable Bottle
 Amazon.com
31 –
Pants
 Amazon.com
32 –
Shirt
 Amazon.com
33 –
Wallet
 Amazon.com
34 –
Small Pillow
 Amazon.com
35 –
Socks
 Amazon.com
36 –
Hairbrush
 Amazon.com
37 –
Can
 Local Convenience Store
38 –
Coin
 Local Convenience Store
39 –
Walking Cane
 Amazon.com
40 –
Wrist Watch
 Amazon.com
41 –
Scissors
 Amazon.com
42 –
Purse
 Amazon.com
43 –
Lighter
 Amazon.com

Publications

Robotic Playmates

When young children play, they often manipulate toys that have been specifically designed to accommodate and stimulate their perceptual-motor skills. Robotic playmates capable of physically manipulating toys have the potential to engage children in therapeutic play and augment the beneficial interactions provided by overtaxed care givers and costly therapists. To date, assistive robots for children have almost exclusively focused on social interactions and teleoperative control. This project represents progress towards the creation of robots that can engage children in manipulative play.

Alex Trevor and Prof. Charlie Kemp in collaboration with Prof. Ayanna Howard and the HumAns Lab are the main investigators on this project which has been generously funded by the Center for Robotics and Intelligent Machines (RIM@GT).

Teleoperation for Mobile Manipulation

Force Feedback Teleoperation on Performing Hygiene Tasks

We expect that haptic teleoperation of compliant arms would be especially important for assistive robots that are designed to help older adults and persons with disabilities perform activities of daily living (ADL). Research has shown that brushing teeth, shaving, cleaning and washing are high priority hygiene tasks for people with disabilities. We describe a teleoperated assistive robot that uses compliant arms and provides force feedback to the operator. We also present one of the first user studies to look at how force feedback and arm stiffness influence task performance when teleoperating a very low stiffness arm.

Teleoperation System

The teleoperation system consists of a master console and a slave robot. The slave robot is Cody (Fig. 1a) and we designed and attached a flat, 3D-printed, spatula-like end effector (Fig. 1b) to resemble an extended human hand. We attached white board eraser felt to the bottom of this end effector. The master console (Fig. 1c) consists of two PCs and a pair of PHANToM Omni (Sensable Technology) haptic interfaces that provide force feedback in position only.

 

Featured Videos

Force Feedback Teleoperation of Cody to perform a Cleaning Task: An operator teleoperating Cody to perform a simulated hygiene task by cleaning dry-erase marks off a mannequin.

Effects of Force Feedback and Arm Compliance on Teleoperation

We conducted a pilot study to investigate the effects of force feedback and arm compliance on the performance of a simulated hygiene task. In this study, each subject (n=12) teleoperated a compliant arm to clean dry-erase marks off a mannequin with or without force feedback, and with lower or higher stiffness settings for the robot’s arm. Under all four conditions, subjects successfully removed the dry-erase marks, but trials performed with stiffer settings were completed significantly faster. The presence of force feedback significantly reduced the mean contact force, although the trials took significantly longer. Refer to the publications for a more detailed information:

 

The mean contact forces for each block: FC block uses the compliant arm with force feedback; FS block uses the stiffer arm with force feedback; NC block uses the com- pliant arm without feedback; and NS block uses the stiffer arm without feedback. Error bars show standard error of the mean. Bars with the same letter were not significantly different, while A and B were (p<0.01).

 

Histogram of the mean completion time: all trials with force feedback (FB) ver- sus without force feedback (No FB); all trials using the compliant setting (Comp) versus trials using the stiffer setting (Stiff). Error bars show standard error of the mean.

Publications

 

Support

Our work is generously supported in part by the NSF grant IIS-0705130.

 

Collaborators

HumAnS Lab