James Kuffner. The Robotics Institute Carnegie Mellon University. Digital Human Research Center (AIST) James Kuffner (CMU/Google)

Transcription

1 James Kuffner The Robotics Institute Carnegie Mellon University Digital Human Research Center (AIST) 1

2 Stanford University University of Tokyo JSK Lab Carnegie Mellon University The Robotics Institute 2001-present Digital Human Research Center (AIST) 2001-present H5 H6 H7 Asimo HRP2 HRP3 2

3 INPUT Computer Program OUTPUT ROBOTICS Robot / World changes ACT SENSE Model of the world Robot / World State Motor Commands (behviors, pos/vel, torques) PLAN 3

4 x = f (x,u) Use a "Forward Simulator" (can be analytic or numerical) Employ heuristics to guide the search towards more promising actions (expected reward) Can cache previously planned results and outcomes to improve heuristics (adaptation and Machine Learning)

5 Bellman, Djikstra, A* Optimal solutions High memory and CPU costs Low-dimensional! limited to toy problems Practical approach: Just give me something that works Sacrifice optimality, completeness Implementable on a real robot 5

6 Uncertainty Prior models Perception Control Search Space Continuous High-dimensional Hard, real-time constraints 6

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8 LEFT CAMERA RIGHT CAMERA PLANNED FOOTSTEP SEQUENCE Footstep planning among obstacles for biped robots [ Kuffner, Nishiwaki, Kagami, Inaba, Inoue, IROS2001 ] 8

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10 Plan for all degrees of freedom Footstep Planning Abstract away all leg details Computationally expensive Uses the full capabilities of the robot Fast Ignores leg capabilities 10

11 (x,y,!) footstep locations relative to stance foot Fixed sampling of possible footsteps 11

12 a 2 x 2 Input a 1 x 1 Successor function x How do you choose the set of actions? 12

13 Double Support Single Support Single Support Double Support 13

14 State Representation: (x, y,!, leg) of current stance foot roll, pitch, and height determined by terrain shape Height map terrain (x,y) 14

15 Angle Input Terrain Roughness 1 N $ c #Cells h c " h p Largest Bump Metric Evaluation Stability Safety / Surroundings max c "SurrCells (h c ) 15

16 Angle Roughness Largest Bump Stability Safety All 16

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18 [Kuffner, Nishiwaki, Kagami, Inaba & Inoue, ICRA 2003] 18

19 [Chestnutt, Kuffner, Nishiwaki, Kagami, Inaba & Inoue, 2003] 19

20 [ Chestnutt, Michel, Kuffner, Kanade, IROS 2007 ] 20

21 [ Chestnutt, Michel, Nishiwaki, Kagami, Kuffner, Kanade, 2006 ]

22 [ Michel, Chestnutt, Nishiwaki, Kagami, Kuffner, Kanade, IROS 2007 ]

23 Dimensionality Reduction: Plan in the low-dimensional space of contact configurations (stances). Delay Trajectory Generation: Avoid dealing with full dynamics in the inner loop of the planner (approximate path existence between stances by exploiting controller limits). Use Fast Metrics: Evaluate footstep candidates for stability and properties needed by the controller. 23

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25 High-level planning Mid-level planning Low-level planning

26 [ Joel Chestnutt, CMU PhD Thesis, 2006 ]

27 Helps the low-level planner search in the right direction Used as a heuristic, not a more detailed plan Plans outward from goal using a grid-based planner Store cost to reach each location

28 Generally more informed Not admissible Can overestimate or underestimate in certain environments Performs better the closer the optimal footstep path can be followed by a mobile robot

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33 [ Chestnutt, Nishiwaki, Kagami, Kuffner, Humanoids 2007 ]

34 [ M. Zucker, CMU PhD thesis, 2010 ]

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37 [ Chestnutt, Takaoka, Suga, Nishiwaki, Kuffner, Kagami, IROS 2009 ] 37

38 [ Nishiwaki, Kagami, et. Al., 2010 ] 38

39 Perception PSF surface map generation Online stereo vision updates every cycle Online region tracking at 15-60Hz Online 3D point cloud env. modeling Planning One-shot footstep planner. Replanning 1000ms at 5K steps per cycle Replanning 800ms at 12K steps per cycle Replanning 800ms at >20K with online adj. Trajectory Generation Fixed, pre-computed stepping motions. Fixed stepping motions with online adjustment Online trajectory generation with partial step adjustment Online generation of leg, body, and step location trajectories Control Cart-table ZMP feedback Cart-table ZMP feedback Extended Torso ZMP feedback Whole-body ZMP feedback Step Cycle Time (ms) ,000 25,000 Footsteps searched per cycle

40 SENSE ACT PLAN Fast replanning Relationship to Receding Horizon Control Hierarchical planning 40

41 Reactive control! direct mapping from sensor inputs to control actions Look-up tables of states to actions Vector fields Feedback control policies POMDPs Limited by what you can store in memory Bag of plans Trajectory libraries / motor primitives How to select appropriate action template? 41

42 Optimality: What is the ideal cost function? Robustness & Uncertainty: Gracefully handle both sensing, modeling, and control noise Improvement over time: Exploit historical data to learn better cost functions. Search-based AI: Unification of Planning, Optimization, and Reinforcement Learning? 42

43 Joel Chestnutt Satoshi Kagami Koichi Nishiwaki Matt Zucker Chris Atketson Drew Bagnell Martin Stolle Phil Michel Yutaka Takaoka Keisuke Suga Rosen Diankov Takeo Kanade Dmitry Berenson Mike Stilman Manfred Lau