Jivko Sinapov, Kaijen Hsiao and Radu Bogdan RusuProprioceptive Perception for Object Weight Classification

What is Proprioception? It is the sense that indicates whether the body is moving with required effort, as well as where the various parts of the body are located in relation to each other.- Wikipedia

*Why Proprioception?

*Why Proprioception?FullEmpty

HardSoftvsWhy Proprioception?

Lifting: gravity, effort, etc.

Pushing: friction, mass, etc.

Squeezing: compliance, flexibility

Power, Play and Exploration in Children and Animals, 2000

Related Work: Proprioception Learning Haptic Representations of Objects:

[ Natale et al (2004) ]

Related Work: Proprioception Proprioceptive Object Recognition

[ Bergquist et al (2009) ]

Perception Problem for PR2:Is the bottle full or empty?

General ApproachLet the robot experience what full and empty bottles feel like

Use prior experience to classify new bottles as either full or empty

Behavior:Power, Play and Exploration in Children and Animals, 2000

Behaviors1) Unsupported Holding2) Lifting

Data RepresentationBehavior Execution:[Ji, Ei, Ci ]Recorded Data:Joint PositionsEffortsClass Label{full, empty}

ExampleRecorded Joint Efforts of Left Arm:

Classification Procedure[Ji, Ei, ?]Feature ExtractionRecognition Model

Recognition ModelX =[Ji, Ei, ?]Recognition Model

Recognition ModelX =[Ji, Ei, ?]Recognition ModelFind N closest neighbors to X in joint-feature space

Recognition ModelX =[Ji, Ei, ?]Recognition ModelFind N closest neighbors to X in joint-feature spaceTrain classifier C on the N neighbors that maps effort features to class label

Recognition ModelX =[Ji, Ei, ?]Recognition ModelFind N closest neighbors to X in joint-feature spaceTrain classifier C on the N neighbors that maps effort features to class labelUse trained classifier C to label X

Objects:Procedure:Place object on tableRobot grasps it and performs the current behavior (either hold or lift) in a random position in space Robot puts object back down on table in random position; repeat. Each behavior performed 100 times on each bottle in both full and empty statesA total of 2 x 5 x 100 x 2 = 2000 behavior executionsTraining Procedure

Evaluation5 fold cross-validation: at each iteration, data with 4 out of the five bottles is used for training, and the rest used for testing

Three classification algorithms evaluated:K-Nearest NeighborsSupport Vector Machine (quadratic kernel)C4.5 Tree

Chance Accuracy: 50%

Can the robot boost recognition rate by applying a behavior multiple times?

How much training data is necessary?

Application to RegressionX =[Ji, Ei, ?]Recognition ModelFind N closest neighbors to X in joint-feature spaceTrain regression model C on the N neighbors that maps effort features to class labelUse trained regression model C to label X

Regression Results

Regression ResultsMean Abs. Error = 0.08827 lbs

Regression ResultsMean Abs. Error = 0.08827 lbsChance error = 0.2674 lbs

Application to Sorting Task Sorting task:

Place empty bottles in trash

Move full bottles on other side of table

Application to Sorting Task

Application to Sorting Task

Sorting Task: video

Application to a new recognition taskFull or empty?

Behavior:

40 trials with full box and 40 trials with empty boxRecognition Accuracy: 98.75 % (all three algorithms)

slide object across table

Sliding task: video

ConclusionBehavior-grounded approach to proprioceptive perception

Implemented as a ROS package:http://www.ros.org/wiki/proprioception

This work has been submitted to ICRA 2011.

Future Work More advanced proprioceptive feature extraction

Multi-modal object perception: Auditory 3D Tactile

Note: experiments with lift behavior were done after the ones with the hold behavior