[Submitted on 17 Jul 2026]
Title:Differentiable Reinforcement Learning for Path Tracking by an Agile Fish-Like Robot
View a PDF of the paper titled Differentiable Reinforcement Learning for Path Tracking by an Agile Fish-Like Robot, by Prashanth Chivkula and 2 other authors
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Abstract:Fish-like swimming has inspired the design of several dozens if not hundreds of bioinspired robots in the last few decades. But the control and motion planning of such robots has been challenging due to the poorly modeled fluid-structure interaction and the nonlinear underactuated dynamics of such robots. While reinforcement learning has allowed significant advances in the context of ground and aerial robots, the lack of a suitable simulation environment with appropriate computational speed and accuracy have prevented similar progress for fish-like robots. We address this two-fold problem by developing a simulation platform that approximates the motion of our fish-like robot with computational efficiency. Then the motion control and path tracking by the robot is performed using PID control where the (variable) gains are learned using back propagation through time and training on a curriculum. The policy learned in the simulation is then applied on the physical platform, demonstrating an excellent match.
Comments: Accepted to IROS 2026, 9 pages, 12 Images, 1 Table
Subjects:
Robotics (cs.RO); Systems and Control (eess.SY)
Cite as: arXiv:2607.16508 [cs.RO]
(or arXiv:2607.16508v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.16508
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Kartik Loya [view email] [v1] Fri, 17 Jul 2026 21:02:17 UTC (14,096 KB)
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