Is Motion Planning Overrated? Jeannette Bohg - Interactive Perception and Robot Learning Lab - Stanford

Transcription

1 Is Motion Planning Overrated? Jeannette Bohg - Interactive Perception and Robot Learning Lab - Stanford

2 Is Motion Planning Overrated? Jeannette Bohg - Interactive Perception and Robot Learning Lab - Stanford tl;dr; Yes

3 Is Motion Planning Overrated? Jeannette Bohg - Interactive Perception and Robot Learning Lab - Stanford

4 Is Motion Planning Overrated? Jeannette Bohg - Interactive Perception and Robot Learning Lab - Stanford tl;dr; It depends

5 Dynamic and Uncertain Environment Robot System Design

6 Dynamic and Uncertain Environment Robot System Design

7 Dynamic and Uncertain Environment Robot System Design Real-Time Perception meets Reactive Motion Generation

8 World Model Object Tracker Camera Arm Tracker Joint Sensors Motion Optimizer Robot State Acceleration Policies World Interaction Robot Torques Inverse Dynamics Controller Kappler et al. Real-Time Perception meets Reactive Motion Generation. RA-L + ICRA 18. Finalist 2018 Amazon Best Systems Paper

9 One time step in the system

10 One time step in the system Raw Sensory Data 30Hz-1kHz

11 One time step in the system Raw Sensory Data Processed Sensory Data 30Hz-1kHz 15-30Hz

12 One time step in the system Raw Sensory Data Processed Sensory Data Local and optimised policies 30Hz-1kHz 15-30Hz 30kHz 5-10Hz

13 One time step in the system Raw Sensory Data Processed Sensory Data Local and optimised policies Fused Policy 30Hz-1kHz 15-30Hz 30kHz 5-10Hz 30 khz

14 One time step in the system Raw Sensory Data Processed Sensory Data Local and optimised policies Fused Policy 30Hz-1kHz 15-30Hz 30kHz 5-10Hz 30 khz Robot Controlled at 1kHz

15 System-Level Evaluation

16 System-Level Evaluation Sense-Plan-Act

17 System-Level Evaluation Sense-Plan-Act Feedback Control

18 System-Level Evaluation Sense-Plan-Act Feedback Control Full System

19 System-Level Evaluation Static Pick and Place Dynamic Pick and Place Dynamic Grasping Dynamic Pointing

20 System-Level Evaluation Static Pick and Place Dynamic Pick and Place Dynamic Grasping Dynamic Pointing

21

22

23 Is Motion Planning Overrated? Jeannette Bohg - Interactive Perception and Robot Learning Lab - Stanford tl;dr; It depends

24

25 Environment Complexity

26 Success of Feedback Control Environment Complexity

27 Success of Feedback Control Environment Complexity

28 Success of Feedback Control Real-Time Perception meets Reactive Motion Generation. Kappler et al. ICRA 18 + RAL. Environment Complexity

29 Success of Feedback Control Real-Time Perception meets Reactive Motion Generation. Kappler et al. ICRA 18 + RAL. QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation. Kalashnikov et al. To appear at CORL 18. Environment Complexity

30 Success of Feedback Control Real-Time Perception meets Reactive Motion Generation. Kappler et al. ICRA 18 + RAL. Motion Planning QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation. Kalashnikov et al. To appear at CORL 18. Environment Complexity

31 Success of Feedback Control Real-Time Perception meets Reactive Motion Generation. Kappler et al. ICRA 18 + RAL. Reactive Motion Planning QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation. Kalashnikov et al. To appear at CORL 18. Environment Complexity

32 Take-Home from Systems Paper

33 Take-Home from Systems Paper Higher complexity - More planning

34 Take-Home from Systems Paper Higher complexity - More planning More uncertainty and dynamics - Online Re-Planning

35 Controlling through contact Finger Object Goal

36 Controlling through contact

37 Controlling through contact

38 Controlling through contact

39 Controlling through contact

40 Controlling through contact

41 Controlling through contact Better Models Better Feedback

42 Predictive Model

43 Predictive Model Model

44 Predictive Model Sensory Observations Model

45 Predictive Model Sensory Observations Model Action

46 Predictive Model Sensory Observations Model Action

47 Predictive Model Sensory Observations Model Predicted Effect Action

48 Predictive Model Sensory Observations Model Predicted Effect Action Model-Predictive Control

49 Predicting physical effect Goal

50 Predicting physical effect Goal

51 Predicting physical effect Goal

52 Keep Predicting Goal

53 Keep Predicting Goal

54 Our Hypothesis

55 Our Hypothesis Physics Models

56 Our Hypothesis Physics Models +

57 Our Hypothesis Physics Models + Learning

58 Our Hypothesis Physics Models + Learning = Generalization

59 Hybrid Model Sensory Observations Learned Model Parameters Physics-based Model Predicted Effect Action

60 Hybrid Model Sensory Observations End-to-End Loss on Effect Learned Model Parameters Physics-based Model Predicted Effect Action

61 Hybrid Model Extrapolation Sensory Observations End-to-End Loss on Effect Learned Model Parameters Physics-based Model Predicted Effect Action

62 Testing Hypothesis on a Case Study More than a Million Ways to Be Pushed. A High-Fidelity Experimental Dataset of Planar Pushing. Yu et al. IROS 2016.

63 Testing Hypothesis on a Case Study More than a Million Ways to Be Pushed. A High-Fidelity Experimental Dataset of Planar Pushing. Yu et al. IROS 2016.

64 Testing Hypothesis on a Case Study More than a Million Ways to Be Pushed. A High-Fidelity Experimental Dataset of Planar Pushing. Yu et al. IROS 2016.

65 Analytic Model x x x x K. M. Lynch, H. Maekawa, and K. Tanie, Manipulation and active sensing by pushing using tactile feedback, in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, vol. 1, Jul 1992, pp

66 Analytic Model x x x x K. M. Lynch, H. Maekawa, and K. Tanie, Manipulation and active sensing by pushing using tactile feedback, in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, vol. 1, Jul 1992, pp

67 Analytic Model x x x 1. Stage x K. M. Lynch, H. Maekawa, and K. Tanie, Manipulation and active sensing by pushing using tactile feedback, in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, vol. 1, Jul 1992, pp

68 Analytic Model x x x 1. Stage x K. M. Lynch, H. Maekawa, and K. Tanie, Manipulation and active sensing by pushing using tactile feedback, in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, vol. 1, Jul 1992, pp

69 Analytic Model x x x 1. Stage x K. M. Lynch, H. Maekawa, and K. Tanie, Manipulation and active sensing by pushing using tactile feedback, in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, vol. 1, Jul 1992, pp

70 Analytic Model 2. Stage x x x 1. Stage x K. M. Lynch, H. Maekawa, and K. Tanie, Manipulation and active sensing by pushing using tactile feedback, in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, vol. 1, Jul 1992, pp

71 Compared Architectures Neural Network only Sensory Observations Learned Model Predicted Effect Action

72 Compared Architectures Neural Network only Hybrid Model Sensory Observations Sensory Observations Learned Model Learned Model Predicted Effect Action Action Parameters Physicsbased Model Predicted Effect

73 Compared Architectures Neural Network only Hybrid Model Error Model Sensory Observations Sensory Observations Learned Model Sensory Observations Learned Model Learned Error Learned Model Predicted Effect Action Action Parameters Model Predicted Effect Action Parameters Physicsbased Physicsbased Model Predicted Effect +

74 Compared Architectures Raw Sensory Observations Neural Network only Hybrid Model Error Model Sensory Observations Sensory Observations Learned Model Sensory Observations Learned Model Learned Error Learned Model Predicted Effect Action Action Parameters Model Predicted Effect Action Parameters Physicsbased Physicsbased Model Predicted Effect +

75 Compared Architectures Raw Sensory Observations Neural Network only Hybrid Model Error Model Sensory Observations Sensory Observations Learned Model Sensory Observations Learned Model Learned Error Learned Model Predicted Effect Action Action Parameters Model Predicted Effect Action Parameters Physicsbased Physicsbased Model Predicted Effect + Training: End-to-End Loss: Error between Predicted and Ground Truth Effect

76 Testing Data Efficiency

77 Testing Data Efficiency

78 Testing Data Efficiency

79 Testing Data Efficiency

80 Testing Generalization Alina Kloss et al, Combining learned and analytical models for predicting action effects, Submitted Pre-print on arxiv.

81 Testing Generalization New Pushing Angles & Contact Points Training Testing Interpolation Alina Kloss et al, Combining learned and analytical models for predicting action effects, Submitted Pre-print on arxiv.

82 Testing Generalization New Pushing Angles New Push Velocities & Contact Points Training Testing Training Testing Interpolation Extrapolation Alina Kloss et al, Combining learned and analytical models for predicting action effects, Submitted Pre-print on arxiv.

83 Testing Generalization New Pushing Angles & Contact Points New Push Velocities New Object Shapes Training Testing Training Testing Training Testing Interpolation Extrapolation Alina Kloss et al, Combining learned and analytical models for predicting action effects, Submitted Pre-print on arxiv.

84 Generalization to new push velocities Training Testing

85 Generalization to new push velocities Training Testing Extrapolation

86 Generalization to new push velocities Training Testing Extrapolation

87 Generalization to new push velocities Training Testing

88 Generalization to new push velocities Training Testing Extrapolation

89 Generalization to new push velocities Training Testing

90 Generalization to new push velocities Training Testing Extrapolation

91 Generalization to new push velocities Training Testing

92 Generalization to new push velocities Training Testing Extrapolation

93 Don t throw away structure

94 Learn to extract given state representation from raw data

95 A Concrete Suggestion Sensory Observations End-to-End Loss on Effect Learned Model Parameters Physics-based Model Predicted Effect Action

96 Interpretability

97 Interpretability Real and Predicted Box Position after 200 identical pushes Representation interpretable

98 Compensation for Errors in Analytical Model Wrong Friction Parameters of Analytical Model

99 Future Direction Sequence of Sensory Observation Learned Model Parameters Physics-based Model Predicted Effect Action

100 Future Direction Sequence of Sensory Observation Learned Model Parameters Physics-based Model Predicted Effect Action Multistep Prediction

101 Future Direction Sequence of Sensory Observation Backpropagation w. r. t. control Learned Model Parameters Physics-based Model Predicted Effect Action Multistep Prediction

102 Models will never be perfect

103 Differentiable Recursive Filtering Jonschkowski et al. RSS 18 Haarnoja et al. NIPS 16 Karkus et al. arxiv 18 Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors. Jonschkowski et al. RSS 18

104 Learning Heteroscedastic Noise On Learning Heteroscedastic Noise Models within Differentiable Filtering. Kloss and Bohg. Submitted to ICLR 19. Picture adapted from: Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors. Jonschkowski et al. RSS 18

105 Learning Heteroscedastic Noise Observation Noise On Learning Heteroscedastic Noise Models within Differentiable Filtering. Kloss and Bohg. Submitted to ICLR 19. Picture adapted from: Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors. Jonschkowski et al. RSS 18

106 Learning Heteroscedastic Noise Observation Noise Process Noise On Learning Heteroscedastic Noise Models within Differentiable Filtering. Kloss and Bohg. Submitted to ICLR 19. Picture adapted from: Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors. Jonschkowski et al. RSS 18

107 Learning Heteroscedastic Noise Observation Noise Process Noise On Learning Heteroscedastic Noise Models within Differentiable Filtering. Kloss and Bohg. Submitted to ICLR 19. Picture adapted from: Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors. Jonschkowski et al. RSS 18

108 Learning Heteroscedastic Noise Observation Noise Process Noise On Learning Heteroscedastic Noise Models within Differentiable Filtering. Kloss and Bohg. Submitted to ICLR 19. Picture adapted from: Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors. Jonschkowski et al. RSS 18

109 Four Non-Linear Filters

110 Four Non-Linear Filters Extended Kalman Filter

111 Four Non-Linear Filters Extended Kalman Filter Unscented Kalman Filter (UKF)

112 Four Non-Linear Filters Extended Kalman Filter Unscented Kalman Filter (UKF) Monte-Carlo UKF

113 Four Non-Linear Filters Extended Kalman Filter Unscented Kalman Filter (UKF) Monte-Carlo UKF Particle Filter

114 Four Non-Linear Filters Process Model Observation Model Extended Kalman Filter Unscented Kalman Filter (UKF) Monte-Carlo UKF Particle Filter

115 Four Non-Linear Filters Process Model Observation Model Extended Kalman Filter Unscented Kalman Filter (UKF) Monte-Carlo UKF Particle Filter

116 Four Non-Linear Filters Process Model Observation Model Extended Kalman Filter Unscented Kalman Filter (UKF) Monte-Carlo UKF Particle Filter

117 Four Non-Linear Filters Process Model Observation Model Extended Kalman Filter Unscented Kalman Filter (UKF) Monte-Carlo UKF Particle Filter

118 Four Non-Linear Filters Process Model Observation Model Extended Kalman Filter Unscented Kalman Filter (UKF) Monte-Carlo UKF Particle Filter

119 Four Non-Linear Filters Process Model Observation Model Extended Kalman Filter Unscented Kalman Filter (UKF) Monte-Carlo UKF Particle Filter

120 Four Non-Linear Filters Process Model Observation Model Extended Kalman Filter Unscented Kalman Filter (UKF) Monte-Carlo UKF Particle Filter

121 Loss Function

122 Loss Function Ground Truth State Sequence

123 Loss Function Ground Truth State Sequence Sequence of State Estimates (Mean and Covariance)

124 Loss Function Ground Truth State Sequence Sequence of State Estimates (Mean and Covariance) Network Weights

125 Loss Function Negative log-likelihood of true state given believe Ground Truth State Sequence Sequence of State Estimates (Mean and Covariance) Network Weights

126 Loss Function Negative log-likelihood of true state given believe Estimation Error Ground Truth State Sequence Sequence of State Estimates (Mean and Covariance) Network Weights

127 Loss Function Negative log-likelihood of true state given believe Estimation Error Regularization Ground Truth State Sequence Sequence of State Estimates (Mean and Covariance) Network Weights

128 Two Application Kitti Visual Odometry Task Planar Pushing Task

129 Visual Odometry Learned Constant Observation Noise Learned Hsc. Process Noise Learned Constant Noises Mixed

130 Visual Odometry Learned Constant Observation Noise Learned Hsc. Process Noise Learned Constant Noises Mixed

131 Visual Odometry Learned Constant Observation Noise Learned Hsc. Process Noise Learned Constant Noises Mixed

132 Visual Odometry Learned Constant Observation Noise Learned Hsc. Process Noise Learned Constant Noises Mixed

133 Visual Odometry Learned Constant Observation Noise Learned Hsc. Process Noise Learned Constant Noises Mixed

134 Planar Pushing Badly-Tuned Noise Well-Tuned Noise Learned Constant Observation Noise Learned Hsc. Process Noise Learned Constant Noises Mixed

135 Planar Pushing Badly-Tuned Noise Well-Tuned Noise Learned Constant Observation Noise Learned Hsc. Process Noise Learned Constant Noises Mixed

136 Planar Pushing Badly-Tuned Noise Well-Tuned Noise Learned Constant Observation Noise Learned Hsc. Process Noise Learned Constant Noises Mixed

137 Planar Pushing Badly-Tuned Noise Well-Tuned Noise Learned Constant Observation Noise Learned Hsc. Process Noise Learned Constant Noises Mixed

138 Planar Pushing Badly-Tuned Noise Well-Tuned Noise Learned Constant Observation Noise Learned Hsc. Process Noise Learned Constant Noises Mixed

139 Planar Pushing Badly-Tuned Noise Well-Tuned Noise Learned Constant Observation Noise Learned Hsc. Process Noise Learned Constant Noises Mixed

140 Take Home

141 Take Home Differentiable EKF most accurate and robust to inaccurate noise models

142 Take Home Differentiable EKF most accurate and robust to inaccurate noise models Particle filter gains most from heteroscedastic noise model.

143 Take Home Differentiable EKF most accurate and robust to inaccurate noise models Particle filter gains most from heteroscedastic noise model. UKF works best for more complex dynamics models.

144 Take Home Differentiable EKF most accurate and robust to inaccurate noise models Particle filter gains most from heteroscedastic noise model. UKF works best for more complex dynamics models.

145 Take Home Differentiable EKF most accurate and robust to inaccurate noise models Particle filter gains most from heteroscedastic noise model. UKF works best for more complex dynamics models. Uncertainty bounds for long-term predictions.

146 Conclusions

147 Conclusions Environment Complexity

148 Conclusions Success of Feedback Control Environment Complexity

149 Conclusions Success of Feedback Control Environment Complexity

150 Conclusions Success of Feedback Control Motion Planning Environment Complexity

151 Conclusions Success of Feedback Control Reactive Motion Planning Environment Complexity

152 Conclusions Success of Feedback Control Reactive Motion Planning Better Predictive Models Better Feedback O P Environment Complexity

153 Thank you for your Attention! Stanford MPI, USC iprl.stanford.edu