Robotics Today

Robotics Today
Pablo Zegers
[email protected]
Autonomous Machines Center
Facultad de Ingenierı́a y Ciencias Aplicadas
Universidad de los Andes
Chile
2013
Zegers
Robotics Today
Robotics
Search for an Autonomous Machine
Duck of Vaucanson (1739).
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Robotics Today
What Is Driving Robotics Today?
A Roadmap for U.S. Robotics: From Internet to Robotics, March 20, 2013
“Three factors drive the adoption of robots:
1. improved productivity in the increasingly competitive
international environment;
2. improved quality of life in the presence of a significantly
aging society; and
3. removing first responders and soldiers from the immediate
danger/action.
Economic growth, quality of life, and safety of our first responders
continue to be key drivers for the adoption of robots.”
Zegers
Robotics Today
Important Advances in Mobile Robotics
Similar Efficiency to that of Animals
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1 kW at 22km/h.
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Three phase permanent
magnet synchronous
motor that produces
twice the torque.
MIT Cheetah Robot.
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Robotics Today
New Products
Low Cost Manipulation
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Dexterous.
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No programming.
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Ready to operate.
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No cage.
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Check www.universal-robots.com
too!
Baxter, Rethink Robotics.
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Robotics Today
Hybrid Approaches
Leveraging the Best of Everybody!
Robots and People Can Work Faster Together, David Bourne, Director Rapid Manufacturing Lab, Robotics
Institute, Carnegie Mellon University, July 25, 2013.
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Robotics Today
Unstructured Object Manipulation
A Robotic Frontier
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Unseen objects.
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Changing geometries.
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Difficult to define formally.
www.seriouseats.com (June 16, 2011).
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Robotics Today
Folding Towels (Maitin-Shepard et al, 2010)
Pick the Towel Up
Maitin-Shepard et al, 2010
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Towel is randomly placed.
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Pick it and rotate looking for corners.
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Robotics Today
Folding Towels (Maitin-Shepard et al, 2010) (cont.)
Find Grasp Points
Maitin-Shepard et al, 2010
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Look for depth discontinuities consistency through time with
the help of a dense sub-pixel optical flow.
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Use RANSAC to fit corners to border points and find
candidates.
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Stereo correspondence for 3D localization.
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Robotics Today
Folding Towels (Maitin-Shepard et al, 2010) (cont.)
Check and Measure
Maitin-Shepard et al, 2010
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Pull taut and twist to check grasping.
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Fit a rectangle to measure the 3D size.
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Start the more structured section of the task: folding.
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Robotics Today
Folding Towels (Maitin-Shepard et al, 2010) (cont.)
Fold
Maitin-Shepard et al, 2010
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NVIDIA GTX 295 GPU optimized for dense optical flow.
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Intel Core 2 quad core 2.5 GHz CPU.
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Average of 1478 seconds per towel, most spent on grasp point
detection.
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Robotics Today
Folding Towels (Maitin-Shepard et al, 2010) (cont.)
Finish!
Maitin-Shepard et al, 2010
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100% success rate on 50 previously unseen towels.
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Robotics Today
Folding Towels (Maitin-Shepard et al, 2010) (cont.)
Pipeline
Maitin-Shepard et al, 2010
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Robotics Today
Folding Towels (Maitin-Shepard et al, 2010) (cont.)
Lessons
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Sensing and grasping is a single problem.
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3D vision is a cornerstone.
Grasping is a whole universe.
Need of a different hardware.
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Unstructured task is composed of structured subproblems.
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Autonomous operation but far from autonomous learning.
Zegers
Robotics Today
Grasping Things
Typical Task
Saxena et al, 2010
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Different objects, densities, materials, surfaces, etc.
Is it delicate or dangerous?
Where should a robot grasp an object?
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Robotics Today
Grasping Things
Grasping Point Detection
Saxena et al, 2010
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Many objects are designed to be grasped.
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Use synthetic data to train detector.
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Use probabilistic model and maximize likelihood in order to
infer grasping point position.
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Robotics Today
Grasping Things
Examples
Saxena et al, 2010
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Robotics Today
Grasping Things
Results
Saxena et al, 2010
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Robotics Today
Grasping Things
Lessons
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3D vision and 3D models of reality.
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Gripper design.
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Lack of real training data because it is a time-consuming task.
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Synthetic training.
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Some objects require complex sequences in order to be
grasped (i.e. book).
Zegers
Robotics Today
Cortando Pan
Se Usa Un Instrumento
Matı́as Torrealba y Pablo Zegers, Centro de Máquinas Autónomas, Facultad de Ingenierı́a y Ciencias Aplicadas,
Universidad de los Andes, Septiembre, 2013
Zegers
Robotics Today
Cortando Pan
El Experimento
Matı́as Torrealba y Pablo Zegers, Centro de Máquinas Autónomas, Facultad de Ingenierı́a y Ciencias Aplicadas,
Universidad de los Andes, Septiembre, 2013
Zegers
Robotics Today
Cortando Pan
Capturando La Esencia
Matı́as Torrealba y Pablo Zegers, Centro de Máquinas Autónomas, Facultad de Ingenierı́a y Ciencias Aplicadas,
Universidad de los Andes, Septiembre, 2013
Zegers
Robotics Today
Cortando Pan
Lecciones
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Las trayectorias encierran toda la información de la
retroalimentación y el control.
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Hay caracterı́sticas propias del movimiento humano que no
tienen porque imitar un robot.
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Hay espacio para hacer las cosas de otra manera.
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Robotics Today
Conclusion
Future Steps
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Model non-linear and non-rigid systems.
Integrate sensing and grasping:
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Motion primitives:
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3D vision.
Grasping technology.
Better hardware.
Human dynamics offers a good starting point.
Determine building blocks.
Learn to combine them.
Transition from fully human to totally autonomous:
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It will take time.
Integrate simulators.
Annotation tool to generate massive training data from real
data.
Need an interface to transfer knowledge.
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Robotics Today
Keywords
Tools for Searching
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Apprenticeship learning.
Demonstration learning.
Dynamical primitives.
Falling strategies for robots.
Grasping.
Household tasks.
Imitation learning.
Learning from others.
Movement mapping.
Motion primitives.
Motor skill learning.
Robot learning.
Transfer learning.
Zegers
Robotics Today
Who Should Be Watched
Not Many!
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Pieter Abbeel
Sylvain Calinon
Auke Ijspeert
Jun Morimoto
Andrew Ng
Jan Peters
Torsten Reil
Stefan Schaal
Peter Stone
Russ Tedrake
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Robotics Today
Bibliography
Starting Point!
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Abbeel, P., Coates, A., and Ng, A., Autonomous Helicopter Aerobatics through Apprenticeship Learning,
The International Journal of Robotics Research, 2010.
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Calinon, S., D’Halluin, F., Sauser, E. L., Caldwell, D. G., and Billard, A. G., Learning and Reproduction of
Gestures by Imitation: An Approach Based on Hidden Markov Model and Gaussian Mixture Regression,
IEEE Robotics & Automation Magazine, June, 2010.
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Kober, J., and Peters, J., Imitation and Reinforcement Learning: Practical Algorithms for Motor
Primitives in Robotics, IEEE Robotics & Automation Magazine, June, 2010.
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Kruger, V., Herzog, D. L., Baby, S., Ude, A., and Kragic, D., Learning Actions from Observations:
Primitive-Based Modeling and Grammar, IEEE Robotics & Automation Magazine, June, 2010.
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Maitin-Shepard, J., Cusumano-Towner, M., Lei, J., and Abbeel, P., Cloth Grasp Point Detection based on
Multiple-View Geometric Cues with Application to Robotic Towel Folding, Proceedings of the
International Conference on Robotics and Automation, 2010.
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Morimoto, J., Jenkins, O. C., and Toussaint, M., Robot Learning in Practice, IEEE Robotics &
Automation Magazine, June, 2010.
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Saxena, A., Diemeyer, J., Kearns, J, and Ng, A., Robotic Grasping of Novel Objects, Advances in Neural
Information Processing Systems, 2006.
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Schaal, S., and Atkeson, C., Learning Control in Robotics: Trajectory-Based Optimal Control
Techniques, IEEE Robotics & Automation Magazine, June, 2010.
Zegers
Robotics Today