uc berkeley personal robotics-w-updated-vision-slide · 2010. 7. 6. · hartmann, michael jordan,...

UC Berkeley Personal Robotics

Presenter: Arjun Singh

Team leads: Pieter Abbeel, Ruzena Bajcsy, Trevor Darrell, Ken Goldberg, Bjoern

Hartmann, Michael Jordan, Dan Klein, Jitendra Malik, Stuart Russell, Claire Tomlin

Main themes

• Hierarchical planning

• Perception

• Manipulation of deformable objects

– End-to-end laundry– End-to-end laundry

• Learning from demonstrations

– Assembly

Hierarchical Planning for

Mobile Manipulation

• Objective 1: speed & scale up

– Apply recent results in efficient, guaranteed-

optimal "angelic” hierarchical planning • Commit to provably optimal (or “good-enough”) high-level plans

• Prune provably suboptimal high-level plans

[Marthi, Russell & Wolfe 2007, 2008]

• Objective 2: uncertainty

– Incorporate partial observability,

information-gathering, hierarchical plan repair

Hierarchical planning example

Preliminary results

[Jason Wolfe, Bhaskara Marthi, Stuart Russell]

Perception

• Visual object recognition

• Pose regression for grasping

• Detecting people

New Local Features for Visual Object

RecognitionKarayev, Fritz, Fidler, Bradski, Darrell

• Handle transparency

• Learn higher-level representations

• Statistically modeled• Statistically modeled

Advanced Methods for Pose

Regression and GraspingSong, Gu, Malik, Darrell

• Category-level pose estimation using latent HOG descriptors

• Discriminatively-trained variant (Gu)

• Combine with local grasp point detection for better grasping in

cases where category-level knowledge is relevant

Detecting peopleMalik, Darrell, Bajcsy

Manipulation of deformable objectsAbbeel

• Large configuration spaces

– Perceptual challenges: estimation of configuration and/or grasp points

– Manipulation challenges: planning in high-dimensional spacesdimensional spaces

• Current directions

– Visual and manipulation primitives:

• Corner detection, edge tracing, bottom most point detection, …

– Simple “worst-case” simulators

• Practical landmark goal: end-to-end laundry

Preliminary results

[autonomous, 100x]

Learning from demonstrationsAbbeel, Goldberg, Hartmann

• Programming robots can be time-consuming

• Often significantly faster and simpler to

provide demonstrations

• Application areas:• Application areas:

– Robot locomotion

– Autonomous helicopter flight

– Manipulation

• Practical landmark goal: teaching basic assembly

Learning from demonstrations

Summary

• Hierarchical planning

• Perception

Pranav Shah

• Deformable objects

• Learning from demonstrations

Hyun Oh Song

Marco

Cusumano-Towner

Arjun

Singh

Shervin JavdaniJudy Hoffman