Adaptive Robotic Control via Nested Learning: Real-Time Recovery from Unmodeled Dynamics

Abstract

Bridging the gap between simulation-trained DRL controllers and deployed robotic actuators remains an open engineering challenge due to unmodeled dynamics and sensing mismatch. Simulators provide reproducible training data and safe iteration, but their physics fidelity is limited: non-linear and time-varying effects (wear, thermal drift, stiction) are rarely modeled, producing deployment gaps. Traditional mitigation strategies, such as Domain Randomization (DR), typically yield conservative, high-entropy policies that sacrifice precision for generalized robustness. In contrast, traditional Meta-Learning frameworks are often constrained to episodic fine-tuning rather than continuous, lifelong adaptation. In this manuscript, we introduce a Nested Learning Architecture, a novel control paradigm that embeds synaptic plasticity directly into the inference loop. We cast adaptation as a bi-level optimization: an outer objective over meta-parameters and an inner single-step adaptation; gradients are propagated through the inner update to train the meta-initialization. The framework comprises two coupled feedback loops: a high-frequency “Inner Loop” that minimizes local tracking errors via gradient descent during operation, and a low-frequency “Outer Loop” that learns a robust initialization state. We formally examine the stability and convergence characteristics inherent to this hierarchical optimization framework and validate it empirically using the PyBullet physics engine on a manipulator subjected to abrupt, unmodeled damping. Our results indicate that Nested Learning enables trajectory recovery within 5.2 seconds—significantly outperforming standard PPO baselines. Furthermore, energy analysis reveals that the adaptive agent avoids the “gain explosion” typical of PID controllers, optimizing the torque-energy manifold efficiently.

Introduction

The text addresses the challenge of achieving robust robotic autonomy in real-world, non-stationary environments where physical conditions such as friction, wear, payloads, and hardware health continuously change. Traditional robots and deep reinforcement learning (DRL) systems rely on static, post-training policies, which fail when real-world dynamics drift from training conditions. This exposes the stability–plasticity dilemma: robots must retain stable learned behaviors while remaining adaptable enough to respond to unforeseen changes.

Existing mitigation strategies have limitations. Domain Randomization improves robustness but leads to overly conservative behavior and fails under systematic failures. System Identification attempts online parameter estimation but suffers from objective mismatch, reliance on accurate physical models, and poor scalability to complex dynamics. Both approaches struggle to provide reliable long-term adaptability.

To overcome these issues, the paper proposes a Nested Learning Architecture, reframing robustness as continuous online adaptation during inference rather than pre-training robustness. The system uses a bi-level optimization framework:

• A fast inner loop performs real-time gradient-based updates to minimize immediate control errors, acting like a reflex mechanism.

• A slow outer loop meta-learns optimal initial parameters that make fast adaptation stable and effective.

Key contributions include a bi-level control formulation, theoretical stability guarantees via Lyapunov analysis, a computationally efficient Hessian-free approximation enabling real-time execution on embedded hardware, and statistically significant empirical validation. The approach draws from meta-learning, fast weights, and deep equilibrium models while avoiding episodic resets and privileged supervision.

Conclusion

In this paper, we presented a Nested Learning architecture that addresses the critical challenge of Sim-to-Real transfer in robotics. By formulating adaptation as a bi-level optimization problem, we enabled a robotic manipulator to recover from catastrophic actuator seizure in real-time without manual intervention. Data from our trials indicate that Nested Learning breaks the traditional optimality-robustness compromise inherent in Domain Randomization. Instead of freezing policies at deployment, we argue for agile, self-regulating neural systems. This architecture mirrors the rapid adaptation of biological spinal reflexes, suggesting that embedding plasticity directly into the controller is key to achieving true robotic resilience.

References

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Copyright

Copyright © 2025 Sarath Chandiran M, Sowmiya R, Sri Hari Sah JL, Rogith R, S. Senthilrajan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.