A physically viable world model augments 3D Gaussian splats with physics simulation to assess robot route feasibility under simulated terrain changes like flooding, revealing failures not visible in static maps.
Zero to Autonomy in Real-Time: Online Adaptation of Dynamics in Unstructured Environments
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
Autonomous robots must go from zero prior knowledge to safe control within seconds to operate in unstructured environments. Abrupt terrain changes, such as a sudden transition to ice, create dynamics shifts that can destabilize planners unless the model adapts in real-time. We present a method for online adaptation that combines function encoders with recursive least squares, treating the function encoder coefficients as latent states updated from streaming odometry. This yields constant-time coefficient estimation without gradient-based inner-loop updates, enabling adaptation from only a few seconds of data. We evaluate our approach on a Van der Pol system to highlight algorithmic behavior, in a Unity simulator for high-fidelity off-road navigation, and on a Clearpath Jackal robot, including on a challenging terrain at a local ice rink. Across these settings, our method improves model accuracy and downstream planning, reducing collisions compared to static and meta-learning baselines.
verdicts
UNVERDICTED 2representative citing papers
A differentiable framework integrates function encoder-based neural ODEs with predictive control to enable zero-shot adaptation of explicit policies across families of nonlinear systems.
citing papers explorer
-
Path Planning in Physically Viable World Models
A physically viable world model augments 3D Gaussian splats with physics simulation to assess robot route feasibility under simulated terrain changes like flooding, revealing failures not visible in static maps.
-
Zero-Shot Function Encoder-Based Differentiable Predictive Control
A differentiable framework integrates function encoder-based neural ODEs with predictive control to enable zero-shot adaptation of explicit policies across families of nonlinear systems.