Adaptive self-supervised learning for real-time problem solving in autonomous systems
6 affiliations
- 1Department of CSE (AI&ML), Vaagdevi College of Engineering
- 2Department of Computer Science Engineering, Government Polytechnic
- 3Professor, Department of Computer Science and Engineering, DVR & Dr. HS MIC College of Technology
- 4CSE (AI&ML) Department, CVR College of Engineering
- 5Department of Information Technology, Aditya University
- 6Assistant Professor, Department of CSE-AIML, MLR Institute of Technology
Abstract
Autonomous systems must perform safely and efficiently when deployed in the real world, where data distributions drift and no labels are available during deployment. Current approaches either assume stationarity, adapt slowly (and only through sufficiently narrow objectives), or leave learning and explicit safety uncoupled (neglecting stringent latency budgets). We present the safety-aligned, latency-bounded problem solving and continuous, label-free adaptation framework, AdaptSolveAI with SelfSolveNet. It combines a multi-objective self-supervised paradigm (contrastive, masked reconstruction, cross-modal alignment, distillation, and a head informed by the barrier), a shift-aware prioritized replay (novelty, safety margin, Fisher sensitivity), and parameter-efficient updates (adaptors/normalization/low-rank layers) under the hard constraint of a compute budget. That is, a control-barrier safety supervisor minimally corrects actions, and is given the same safety-signal used in the representation learning stage. On driving, navigation, manipulation and scheduling benchmarks with S1–S3 distribution shifts, AdaptSolveAI outperforms the strongest baseline on normalized return (0.86 ± 0.01 vs. 0.79 ± 0.02), success rate (77.9%±1.2 vs. 70.3%±1.3), and safety violations (3.9%±0.3 vs. 5.8%±0.3), while maintaining FPS (frames per second) and deadline missed (%) (≤ 1% of deadlines missed). Time-to-adapt is less (310 ± 20 vs. 520 ± 30 steps) The area under the robustness curve is also higher (0.857 vs. 0.776) under progressive camera dropout, with recovery after sensor restoration taking place faster. Abalations show the performance benefits cannot be achieved without the safety-aware and cross-modal heads, prioritized replay, and adapters. The framework provides a practical recipe for resilient and safety-aware autonomy by enabling continuous learning from unlabeled streams, adaptation within fixed computational budgets, and action execution under control barrier function–based safety constraints. It is directly applicable to embedded deployments and offers a path toward standardized evaluation and certification of learning-enabled systems.
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BibTeX
@article{rajender2026adaptive,
title = {{Adaptive self-supervised learning for real-time problem solving in autonomous systems}},
author = {Nagunuri Rajender and Girish Reddy Ginni and K. M. Rayudu and K. Sowjanya Bharathi and A. V. Lalitha Devi and Seedarla Sandhya Rani},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-62795-7},
url = {https://doi.org/10.1038/s41598-026-62795-7},
note = {CC BY},
}
RIS
TY - JOUR
TI - Adaptive self-supervised learning for real-time problem solving in autonomous systems
AU - Nagunuri Rajender
AU - Girish Reddy Ginni
AU - K. M. Rayudu
AU - K. Sowjanya Bharathi
AU - A. V. Lalitha Devi
AU - Seedarla Sandhya Rani
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-62795-7
UR - https://doi.org/10.1038/s41598-026-62795-7
AB - Autonomous systems must perform safely and efficiently when deployed in the real world, where data distributions drift and no labels are available during deployment. Current approaches either assume stationarity, adapt slowly (and only through sufficiently narrow objectives), or leave learning and explicit safety uncoupled (neglecting stringent latency budgets). We present the safety-aligned, latency-bounded problem solving and continuous, label-free adaptation framework, AdaptSolveAI with SelfSolveNet. It combines a multi-objective self-supervised paradigm (contrastive, masked reconstruction, cross-modal alignment, distillation, and a head informed by the barrier), a shift-aware prioritized replay (novelty, safety margin, Fisher sensitivity), and parameter-efficient updates (adaptors/normalization/low-rank layers) under the hard constraint of a compute budget. That is, a control-barrier safety supervisor minimally corrects actions, and is given the same safety-signal used in the representation learning stage. On driving, navigation, manipulation and scheduling benchmarks with S1–S3 distribution shifts, AdaptSolveAI outperforms the strongest baseline on normalized return (0.86 ± 0.01 vs. 0.79 ± 0.02), success rate (77.9%±1.2 vs. 70.3%±1.3), and safety violations (3.9%±0.3 vs. 5.8%±0.3), while maintaining FPS (frames per second) and deadline missed (%) (≤ 1% of deadlines missed). Time-to-adapt is less (310 ± 20 vs. 520 ± 30 steps) The area under the robustness curve is also higher (0.857 vs. 0.776) under progressive camera dropout, with recovery after sensor restoration taking place faster. Abalations show the performance benefits cannot be achieved without the safety-aware and cross-modal heads, prioritized replay, and adapters. The framework provides a practical recipe for resilient and safety-aware autonomy by enabling continuous learning from unlabeled streams, adaptation within fixed computational budgets, and action execution under control barrier function–based safety constraints. It is directly applicable to embedded deployments and offers a path toward standardized evaluation and certification of learning-enabled systems.
SN - 2045-2322
ER -