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Continual Field-Adaptive Models (CFAMs) for Post-Deployment Physical AI

Summary

CFAMs are a new approach enabling robots to keep learning after deployment using autonomous, gradient-free, on-device updates. Evaluated across five robot types, CFAM matches a fully trained policy with 2.5x less training data, improves near-out-of-distribution action success by 13.9 percentage points, and largely avoids catastrophic forgetting.

SourcearXiv RoboticsAuthor: Amarjot Singh, Tanmay R. Pancholi, Jainam Kothari, Shrirang Mahajan, Ketan Bansal, Zackory Erickson, Giuseppe Loianno, Alexandre M. Bayen, Jeff Schneider, Vince Nakayama
Continual Field-Adaptive Models (CFAMs) for Post-Deployment Physical AI
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[Submitted on 3 Sep 2026]

Title:Continual Field-Adaptive Models (CFAMs) for Post-Deployment Physical AI

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Abstract:Unattended interactive autonomy - machines that step into danger in place of humans and complete tasks with human tools - remains a missing capability in mission-critical operations. These domains offer scarce training data and only onboard compute, yet deployed systems must face novelty without erasing prior competence.

We introduce Continual Field-Adaptive Models (CFAMs), which learn efficiently in the lab and continue learning after deployment through autonomous, gradient-free, on-device updates. CFAM uses a complementary learning architecture with a frozen slow-learning component and a fast-learning Capsule Field. The slow component contains three cortices: Sensor, which maps multimodal input into 3D-grounded geometry; Reasoning, which decomposes tasks into skills and evaluates outcomes; and Action, which executes geometric skills. The Capsule Field stores field learning one-shot and gradient-free as Competence Capsules. Skill installation is few-shot in the lab and continual in the field; open-world novelty is outside scope.

We evaluate CFAM across five embodiments: manipulator, quadruped, humanoid, quadrotor, and off-road vehicle. Baselines (pi0, CogACT, SpatialVLA) use the same in-house multi-embodiment dataset for physical-platform comparisons. CFAM reaches the operating point of a standard policy trained on the full prior-training dataset using 40% of the data, or 2.5x fewer trajectories. At test time, autonomous capture of verified near-OOD cases improves action success by 13.9 percentage points. In sequential simulation, backward transfer is -0.5 percentage points versus -11.4 for LoRA. CFAM therefore provides a bounded form of post-deployment physical intelligence: few-shot skill learning, autonomous field growth from verified near-OOD experience, and retention of prior competence.

Subjects:

Robotics (cs.RO); Artificial Intelligence (cs.AI)

Cite as: arXiv:2609.04552 [cs.RO]

(or arXiv:2609.04552v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.04552

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Amarjot Singh [view email] [v1] Thu, 3 Sep 2026 23:15:53 UTC (3,862 KB)

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Key points and analysis

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Key points

  • CFAM combines a frozen slow-learning component with a fast-learning Capsule Field for one-shot, gradient-free skill acquisition.
  • Tested on manipulator, quadruped, humanoid, quadrotor, and off-road vehicle platforms using the same multi-embodiment dataset.
  • Achieves standard-policy performance using only 40% of the prior-training data (2.5x fewer trajectories).
  • Autonomous capture of verified near-OOD samples improves test-time action success by 13.9 percentage points, with backward transfer of -0.5 points vs -11.4 for LoRA.

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