Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllers
arXiv:2605.25346v1 Announce Type: cross Abstract: Neural network (NN) dynamics models and control policies achieve strong performance in robotics, but providing sound guarantees under uncertainty remains difficult, especially for closed-loop NN systems. Existing reachability tools provide formal over-approximations, yet are often non-differentiable, overly conservative, or too slow for modern learning and online planning pipelines. To address this, we present a parallelizable, differentiable reachability framework in JAX for continuous- and discrete-time systems with analytical and NN-based dy
The increasing deployment of neural networks in critical systems, particularly in robotics, necessitates robust methods for safety and reliability, driving the development of formal verification techniques.
This development addresses a key limitation of AI in high-stakes applications by enabling certified performance guarantees for neural network-controlled systems, boosting their adoption in domains requiring high assurance.
The ability to formally verify and differentiate reachability for neural dynamics transforms the landscape of AI safety and control, making AI more trustworthy for real-world deployment in robotics and autonomous systems.
- · AI/ML developers
- · Robotics companies
- · Autonomous systems manufacturers
- · Safety-critical industries
- · Companies relying on unverified black-box AI
- · Traditional control system designers (without NN integration)
Wider adoption of neural network-based controllers in safety-critical robotics and autonomous vehicles due to enhanced verification capabilities.
Increased investment in differentiable programming frameworks and formal methods for AI, leading to more robust AI engineering practices.
Certification bodies and regulatory frameworks will need to adapt to formal proofs of safety for AI systems, potentially accelerating regulatory approval for AI in new domains.
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Read at arXiv cs.LG