Problem
Research questions and friction points this paper is trying to address.
Sim-to-Real Mismatch
Disturbance Observer
World Models
Robotic Control
Tackling Sim-to-Real Mismatch Through Sampling-Based Disturbance Observers: From Analytical Models to Learned World Models
🤖 AI Summary
This study addresses the simulation-to-reality mismatch in robot control caused by physical disturbances despite accurate models, proposing a sampling-based disturbance observer (DOB). This method overcomes the limitation of classical DOBs that rely on explicit dynamics models by leveraging state rollout and cost query interfaces, thereby extending disturbance compensation to black-box simulators and learned world models. Furthermore, it innovatively decouples the state and cost disturbance channels for independent estimation and compensation. Experimental results demonstrate that the proposed approach effectively bridges the Sim-to-Real gap across diverse simulated and real-world robotic tasks, yielding significant improvements in control performance.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Sampling-Based Disturbance Observer
Sim-to-Real Mismatch
Learned World Models
State-Effect Disturbances
Cost Disturbances
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