Perception for robot autonomy. Perception for robot autonomy

Transcription

1 Perception for robot autonomy Martial Hebert Drew Bagnell Tony Stentz Perception for robot autonomy Sensor input Perception algorithms Interpretation Execution 1

2 Perception for robot autonomy From sensor features to plans and paths DARPA UPI From sensor features to semantic interpretation DARPA MARS One perspective on the perception+autonomy challenges From sensor features to plans and paths Learning and optimal control Generating multiple options Sequential processes From sensor features to semantic interpretation The tractability challenge Generating multiple hypothesis Incorporating external knowledge sources and data Limited computation Deciding when to generate an output Semi-supervised modeling 2

3 From sensor features to plans and paths: The pothole example 3

4 Local features: color, texture, + Context features How to map the features to control/decision policies? From sensor features to plans and paths Even if we know an exact control model for the system: Noisy data + not the right information Hard to model mapping from sensor data to actions and decisions Mapping depends on objective mission Learn mapping from data 4

5 Learning and optimal control Learning to optimally map features to plans and paths X (Sensor (Input) Data) Learning Y Y (Output) (Path to goal) 5

6 Optimal Control Solution Cost Map Learning 2-D Planner Y (Path to goal) Mode 1: Training example 6

7 Mode 1: Training example Mode 1: Learned behavior 7

8 Mode 1: Learned behavior Mode 1: Learned cost map 8

9 Mode 2: Training example Mode 2: Training example 9

10 Mode 2: Learned behavior Mode 2: Learned behavior 10

11 Mode 2: Learned cost map There is no single right mapping between sensor features and actions Mapping depends on subjective operator objectives Learning the mapping: Assume that behavior we wish to imitate can be recovered by a planning/optimal control algorithm Generalization to handle noisy, imperfect behavior Extension to strategic and multi-agent cases 11

12 Generating and evaluating multiple alternatives (semi-)optimal planning/decision given learned cost from sensor features may not be feasible In fact in general it is not possible Alternative: Evaluate a set of alternatives with respect to the learned costs 12

13 Research questions: Optimize content and order of list Find optimal evaluation strategy for: Relevance: Early items are highly likely to succeed Diversity: Enough variation across early items such that redundancy is minimized Online operation Sequential processes Decisions are temporally dependent Cannot learn from individual samples Correlated errors leading to compounding effect 13

14 The dangers of optimistic models Training data sees (almost) only good examples Optimistic modeling, overfitting, catastrophic failure [D. Pomerleau circa 1987] Example: Learning to drive Worst-case quadratic growth in likelihood of errors over time 14

15 Example Results explore the fundamental role of interaction between a teacher and a student learning to mimic a task Formal results show decrease in the error as function of the number of training iterations 15

16 Semantic interpretation Mapping directly from sensor data to plans and paths may not be sufficient Describing the environment in order to reason and make decisions 16

17 The tractability challenge tree building object road Input Joint Inference Output Off-line training: Model the distribution of labels vs. features Run-time inference: Find most likely labels given model 17

18 Multiple hypothesis generation Explaining decisions Explaining mistakes Perfect perception for robotics? Visual input Perception algorithms Interpretation Execution 18

19 Reason about multiple hypothesis Visual input Perception algorithms Execution Predict and refine Representing uncertainty in perception Visual input Perception algorithms Interpretation Application 19

20 Representing ambiguity Predicted P(Class 1) P(Class 2) 20

21 Incorporating external knowledge sources Bottom-up processing from sensor data is not sufficient Example DoT Univ. Transportation Center 21

22 Using prior (approximate) maps How to combine uncertain labels from the map with perception output? Does it improve accuracy? How to represent the uncertainty? Example: Uncertainty modeling P image label = x external information = x = 22

23 Limited computation Anytime processing Distributed processing Computation challenge Onboard computation resources may be insufficient for processing sensor data at high enough rate The situation may call for a (possibly partial) answer sooner than a full computation cycle Essential in robotics/autonomy applications Computational performance is a dominant concern Anytime processing Distributed processing 23

24 Distributed perception processing DARPA/ARL/SEI Graceful degradation based on availability of computationl nodes Automatic selection of onboard data and computation Fast switching One perspective on the perception+autonomy challenges From sensor features to plans and paths Learning and optimal control Generating multiple options Sequential processes From sensor features to semantic interpretation The tractability challenge Generating multiple hypothesis Incorporating external knowledge sources and data Limited computation Deciding when to generate an output Semi-supervised modeling 24