Learned Perceptive Forward Dynamics Model for Safe and Platform-aware Robotic Navigation (2504.19322v2)

Abstract: Ensuring safe navigation in complex environments requires accurate real-time traversability assessment and understanding of environmental interactions relative to the robots capabilities. Traditional methods, which assume simplified dynamics, often require designing and tuning cost functions to safely guide paths or actions toward the goal. This process is tedious, environment-dependent, and not generalizable. To overcome these issues, we propose a novel learned perceptive Forward Dynamics Model (FDM) that predicts the robots future state conditioned on the surrounding geometry and history of proprioceptive measurements, proposing a more scalable, safer, and heuristic-free solution. The FDM is trained on multiple years of simulated navigation experience, including high-risk maneuvers, and real-world interactions to incorporate the full system dynamics beyond rigid body simulation. We integrate our perceptive FDM into a zero-shot Model Predictive Path Integral (MPPI) planning framework, leveraging the learned mapping between actions, future states, and failure probability. This allows for optimizing a simplified cost function, eliminating the need for extensive cost-tuning to ensure safety. On the legged robot ANYmal, the proposed perceptive FDM improves the position estimation by on average 41% over competitive baselines, which translates into a 27% higher navigation success rate in rough simulation environments. Moreover, we demonstrate effective sim-to-real transfer and showcase the benefit of training on synthetic and real data. Code and models are made publicly available under https://github.com/leggedrobotics/fdm.

Overview of Learned Perceptive Forward Dynamics Model for Safe Robotic Navigation

The paper presents a novel approach to robotic navigation through the development of a Learned Perceptive Forward Dynamics Model (FDM), specifically designed for legged robots operating in complex environments. This model facilitates the prediction of a robot's future states by utilizing a variety of observational inputs, including surrounding geometry and proprioceptive measurements, which are incorporated into a sampling-based planning framework. The fundamental ambition of this work is to transcend traditional navigation techniques that rely heavily on simplified dynamics, thus requiring intensive environment-specific tuning. By broadening the navigational capacity of legged robots, the paper strives to develop safer, scalable methods for robotic navigation that embody platform-awareness and environmental adaptability.

One notable result from the paper is the demonstrated improvement in position estimation by a significant margin of 41% over competitive baselines, leading to a corresponding 27% increase in successful navigation within rough simulation environments. The empirical analysis strongly supports the perceptive capacity of the FDM, illustrating its ability to distinguish and anticipate collision risks effectively across various terrain types, including those not covered by rigid body simulations. Such results accentuate the potential of the FDM to deliver a heuristic-free framework for safe robotic navigation, necessitating minimal tuning and offering strong sim-to-real transfer capabilities.

Implications for Future Robotics and AI Development

The methodology proposed in this paper provides a substantial advance in the domain of autonomous robotic navigation, considering both theoretical and practical perspectives. Theoretically, the integration of perceptive dynamics modeling into planning frameworks undoubtedly offers a potent enhancement to robotics research, promising more robust and reliable algorithms adept at handling diverse environmental interactions. Practically, the potential applications span a myriad of fields including search and rescue operations, terrestrial exploration, and infrastructure inspection, wherein robots must often negotiate complex and unpredictable terrains.

Such advancements inevitably drive the evolution of robust models in artificial intelligence, highlighting the significance of perception-driven dynamics in autonomous systems. As these models become increasingly sophisticated, we may expect future developments to incorporate more complex environments and scenarios, potentially progressing beyond mere geometry to encompass more semantic understanding of surroundings.

Future Directions

The deployment of perceptive FDMs in more varied robotic platforms and domains could provide essential insights into the scaling and adaptation of dynamics models, as robotic applications broaden to include higher fidelity environments and interactions. Exploring adaptive timestep models and enriching perceptive layers, possibly moving towards RGB inputs, could form vital pathways to enhance model performance further. Moreover, the extension of FDMs into ensemble frameworks could offer improved uncertainty quantification, thereby enriching failure prediction and safety assurance.

Through exploring these avenues, researchers might uncover methods to further bridge the gap between simulated and real-world dynamics, creating a feedback-rich environment for evolving robotic intelligence capable of mastering increasingly complex navigational challenges.

In summary, the paper delivers a substantive contribution to the field of robotic navigation through its perceptive model and planning framework, thereby setting the stage for future research aimed at expanding the application scope and efficacy of autonomous robotic systems.