Tackling Sim-to-Real Mismatch Through Sampling-Based Disturbance Observers: From Analytical Models to Learned World Models
Authors: Tianqi Zhu, Jun Yang, Jianliang Mao, Cong Li, Shihua Li
Organizations: School of Automation, Key Laboratory of Measurement and Control of CSE, Ministry of Education, Southeast University, Nanjing 210096, China · Robotics and Autonomous Systems Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, China · Faculty of Artificial Intelligence, Shanghai University of Electric Power, Shanghai 200090, China
Robotic controllers increasingly rely on analytical models, simulators, cost-query interfaces, and learned world models. However, physical deployment can deviate from nominal assumptions, and additional disturbances may arise even when the model itself is accurate. In control systems, disturbance observers (DOB) are widely used to estimate such unmeasured effects from nominal models and measured feedback. Classical DOB formulations are generally built around explicit plant models. This paper develops the sampling-based disturbance observer (SDOB), extending the DOB principle to a broader range of models, including simulators and learned world models, through state-rollout or cost-query interfaces. SDOB separates two observable channels: state-effect disturbances, for which the feedback state differs from its prediction, and cost disturbances, for which the same query state receives different costs as the perceived environment changes. Diverse simulation and real-robot experiments across traditional and learned models demonstrate the effectiveness of SDOB in compensating for sim-to-real mismatch and improving control performance.
Figures & tables
Figure 1: Classical frequency-domain DOB control structure.
Figure 2: Overview of SDOB: model classes, disturbance channels, and key advantages.
Figure 3: Two disturbance channels used by SDOB. (a) Illustration under the state-effect disturbance dk−1x . (b) Illustration under the cost disturbance dk−1c .
Figure 4: Physical admissibility of sampled state-effect disturbances.
Figure 5: Cost disturbance estimation and planning compensation. The first two panels query costs at the same locations before and after an environment update. The third panel matches each current query to a position in the previous field with similar surrounding costs. Arrows connect the current queries, marked by white centers, to these matched earlier positions, marked by orange circles. The displacement between them estimates how the local cost pattern has moved. Red rings indicate that the current cost is higher than the matched previous cost, while blue rings indicate that it is lower. The matching minimizes the neighborhood loss in Eq. ( 27 ). The final panel uses the estimated motion to predict future costs and guide planning through Eqs. ( 29 ) and ( 30 ).
Figure 6: Schematic of the SDOB computation flow. Feedback updates the state-effect and cost disturbance estimates for subsequent rollout and cost compensation.
Task
SDOB channel
SDOB state samples mx
SDOB query states Nc
Controller rollout samples
Prediction horizon H
Environment step
Inverted pendulum
State effect
256
–
1024
15
0.050 s
Eight-obstacle navigation
State and cost
256
332
500
30
0.050 s
Latent Reacher
State effect
256
–
300
5
0.020 s
Rocket Collect
Cost
–
289
32
12
0.033 s
Landing
State and cost
256
121
32
24
0.033 s
Table 1: Core SDOB and controller parameters used in simulation.
Figure 7: Representative inverted-pendulum snapshots under sinusoidal torque bias. MPPI+SDOB effectively compensates for the torque disturbance, achieving disturbance-rejection performance comparable to the conventional MPPI+DOB.
Figure 9: Snapshots comparing navigation methods in an eight-obstacle environment with state-effect and cost disturbances. MPPI+SDOB successfully avoids collisions while achieving a shorter navigation path than the comparison methods.
Figure 11: Final Reacher observations from representative evaluations, where MPPI+SDOB achieves a smaller reaching error.
Table 2: Completion times in the representative real-car runs.
Figure 18: PointWorld box-lifting results. (a) Point-cloud motion predictions without and with SDOB. (b) Real-robot results with MPPI. (c) Real-robot results with MPPI+SDOB, showing a smaller box-lifting error.
Metric
MPPI
MPPI+SDOB
Final task RMSE (mm)
55.26±14.99
22.61±12.58
Final lift (mm)
287.39±24.91
306.60±12.73
Table 3: PointWorld box-lifting results over 20 paired trials.
School of Mechanical Engineering, Hanyang University, Seoul 04763, Republic of Korea · Department of Mechanical System Design, Kyonggi University, Suwon 16227, South Korea · School of Electrical Engineering, Kookmin University, Seoul 02707, South Korea