aiqbench analysis
This research introduces DiSA-IQL, an offline reinforcement learning algorithm designed to enhance the control of soft snake robots by mitigating distribution shifts between training and deployment environments. By penalizing unreliable state-action pairs during pre-collected dataset utilization, the method achieves superior robustness and success rates compared to standard baselines like Behavior Cloning and CQL without requiring costly real-time interaction. The work addresses a critical bottleneck in deploying deep RL for complex robotic systems where online experimentation is impractical or dangerous.
DiSA-IQL: Offline Reinforcement Learning for Robust Soft Robot Control under Distribution Shifts
arXiv:2510.00358v2 Announce Type: replace Abstract: Soft snake robots offer remarkable flexibility and adaptability in complex environments, yet their control remains challenging due to highly nonlinear dynamics. Existing model-based and bio-inspired controllers rely on simplified assumptions that limit their performance. Deep reinforcement learning (DRL) has recently emerged as a promising alternative, but online training is often impractical because of costly and potentially damaging real-world interactions. Offline RL provides a safer option by leveraging pre-collected datasets, but it suffers from distribution shift, which degrades generalization to unseen scenarios. To overcome this challenge, we propose DiSA-IQL (Distribution-Shift-Aware Implicit Q-Learning), an extension of IQL that incorporates robustness modulation by penalizing unreliable state-action pairs to mitigate distribution shift. We evaluate DiSA-IQL on goal-reaching tasks across two settings: in-distribution and out-of-distribution evaluation. Simulation results show that DiSA-IQL consistently outperforms baseline models, including Behavior Cloning (BC), Conservative Q-Learning (CQL), and vanilla IQL, achieving higher success rates, smoother trajectories, and greater robustness.
Abstract:Soft snake robots offer remarkable flexibility and adaptability in complex environments, yet their control remains challenging due to highly nonlinear dynamics. Existing model-based and bio-inspired controllers rely on simplified assumptions that limit their performance. Deep reinforcement learning (DRL) has recently emerged as a promising alternative, but online training is often impractical because of costly and potentially damaging real-world interactions. Offline RL provides a safer option by leveraging pre-collected datasets, but it suffers from distribution shift, which degrades generalization to unseen scenarios. To overcome this challenge, we propose DiSA-IQL (Distribution-Shift-Aware Implicit Q-Learning), an extension of IQL that incorporates robustness modulation by penalizing unreliable state-action pairs to mitigate distribution shift. We evaluate DiSA-IQL on goal-reaching tasks across two settings: in-distribution and out-of-distribution evaluation. Simulation results show that DiSA-IQL consistently outperforms baseline models, including Behavior Cloning (BC), Conservative Q-Learning (CQL), and vanilla IQL, achieving higher success rates, smoother trajectories, and greater robustness.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2510.00358 [cs.RO]
(or arXiv:2510.00358v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2510.00358 Focus to learn more arXiv-issued DOI via DataCite
2026 American Control Conference (ACC), pp. 3983-3988, 2026
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Full text under the publisher's open licence · 251 words
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