Title: Learning to Control Free-Form Soft Swimmers
Venue: NeurIPS 2025
Accepted: yes
Keywords: Soft Swimmer Agents

Swimming Simulation Environment

Morphological Generalization

Reinforcement Learning
Abstract: Swimming in nature achieves remarkable performance through diverse morphological adaptations and intricate solid-fluid interaction, yet exploring this capability in artificial soft swimmers remains challenging due to the high-dimensional control complexity and the computational cost of resolving hydrodynamic details. Traditional approaches often rely on morphology-dependent heuristics and simplified fluid models, which constrain exploration and preclude advanced strategies like vortex exploitation. To address this, we propose an automated framework that combines a unified, reduced-mode control space with a high-fidelity GPU-accelerated simulator. Our control space naturally captures deformation patterns for diverse morphologies, minimizing manual design, while our simulator efficiently resolves the crucial fluid-structure interactions required for learning. We evaluate our method on a wide range of morphologies, from bio-inspired to unconventional. From this general framework, high-performance swimming patterns emerge that qualitatively reproduce canonical gaits observed in nature without requiring domain-specific priors, where state-of-the-art baselines often fail, particularly on complex topologies like a torus. Our work lays a foundation for future opportunities in automated co-design of soft robots in complex hydrodynamic environments. The code is available at https://github.com/changyu-hu/FreeFlow.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a unified learning framework for controlling free-form soft-bodied swimmers by combining a reduced-mode internal actuation model with a GPU-accelerated fluid-structure simulator based on the Lattice Boltzmann Method (LBM). The system learns swimming policies through reinforcement learning (SAC) in a morphology-agnostic control space, achieving notably strong performance across a wide range of both bio-inspired and abstract 3D morphologies. Notably, the method does not rely on predefined muscle layouts or hand-crafted actuation heuristics, enabling the emergence of plausible swimming behaviors through physically grounded simulation.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n•\tThe integration of geodesic-based sampling, linear blend skinning (LBS), and LBM-based fluid simulation forms a well-designed and efficient pipeline for learning physically plausible control policies in fluid environments.\n•\tThe method is applied successfully to a broad set of morphologies, including highly unconventional topologies where baseline controllers struggle, demonstrating its robustness and generalizability.\n•\tThe learned controllers exhibit swimming behaviors reminiscent of biological strategies (e.g., undulation, pulsation) without requiring any explicit kinematic priors, which is an appealing result.\n•\tThe authors provide thorough ablation studies and quantitative comparisons to validate the design choices.\n\nWeaknesses\n1.\tLimited behavioral diversity and adaptability\nOne of the defining characteristics of biological swimmers is their ability to exhibit multiple distinct locomotion modes (e.g., hovering, darting, gliding) using the same body structure, and to transition between these modes smoothly in response to environmental changes.\nIn contrast, the proposed system is trained on a single task objective (forward swimming) and results in a single learned gait per morphology. There is currently no mechanism to support multi-modal behaviors or gait switching, which limits the biological plausibility and adaptive capacity of the framework.\n2.\tAmbitious biological framing not fully supported by methodology\nThe paper frequently refers to the \"rediscovery\" or \"understanding\" of evolved swimming strategies, but it does not model evolutionary or developmental processes. Morphologies are fixed, and control policies are optimized independently for each shape. While the resulting behaviors are certainly interesting and sometimes biologically plausible, these claims may be overstated given the actual scope of the method. A more modest and precise framing would strengthen the overall presentation.\n3.\tLimited novelty in methodology\nThe proposed method combines existing components: reduced-mode actuation via LBS, geodesic sampling, LBM-based simulation, and standard SAC reinforcement learning. These are all well-established techniques. The originality lies more in their practical integration than in introducing fundamentally new algorithms or theoretical insights.\n4.\tSimplified task setup\nAll training scenarios focus on a forward swimming task with a fixed target direction and reward function. There is little exploration of more complex objectives such as turning, obstacle avoidance, or energy-efficient maneuvering. This restricts the evaluation of behavioral richness and policy flexibility, and may limit the method’s applicability to real-world adaptive tasks.\n\nQUESTIONS:\n1.\tBehavioral Diversity: Can your framework support learning of multiple distinct swimming strategies (e.g., hovering, turning, darting) within a single morphology? Would introducing multi-task learning or a goal-conditioned policy allow this? Demonstrating this capability would significantly strengthen your claims of biological plausibility.\n2.\tGait Transitions: Is it possible to transition smoothly between different learned gaits within the same controller or policy space? If not, do you foresee how your reduced-mode control representation might be extended to support this?\n3.\tBiological Framing: Could you clarify your use of phrases like “rediscovering evolved behaviors”? As it stands, the methodology does not explicitly model evolutionary processes. Framing this as emergent similarity rather than discovery could help avoid misunderstanding.\n4.\tCo-design Potential: You mention future work on joint morphology-control optimization. Do you believe your current control representation is compatible with a differentiable or evolutionary co-design pipeline?\n5.\tAdditional Tasks: Have you considered extending your experiments to include non-trivial objectives such as turning, avoiding obstacles, or energy-efficient locomotion? Such tasks would better demonstrate policy generalization and control flexibility.\n\nLIMITATIONS:\nWhile the paper discusses some limitations, such as the absence of joint morphology-control co-design, several important points warrant further consideration. First, the method is restricted to learning a single locomotion strategy per morphology and does not support multi-modal behaviors or context-driven gait transitions—key features of biological locomotion. Second, the task setup is narrowly focused on forward swimming, limiting the evaluation of adaptability or robustness across diverse goals. Third, morphologies are fixed and not co-optimized with control, missing the opportunity to study the interdependence of structure and behavior. Finally, the method is evaluated entirely in simulation without addressing real-world deployment challenges such as actuation delay, sensor noise, or sim-to-real transfer. While the framework is promising, acknowledging these aspects would provide a more balanced view of its current scope and future potential.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes an automated framework featuring a unified reduced-mode control space that naturally captures typical deformation patterns observed across diverse and unconventional swimmers, and a GPU-accelerated simulator to efficiently resolve fluid-structure interactions, specifically tailored for learning soft-body swimming controllers. The proposed method is evaluated on 12 soft-body morphologies and benchmarked against domain-expert designed and state of the art (clustering-based and differentiable) control approaches. The proposed approach performs similarly to the state of the art for the biology based swimming morphologies and outperforms the state of the art for abstract swimmers.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nThe paper introduces a framework that unities soft-body control with reduced deformation modes and implemented with a GPU-accelerated simulator. The method is evaluated on 12 diverse morphologies, six intuitive biology-inspired swimmers and six abstract shapes. The evaluation experiments are well-designed and sufficient to support the paper's claims.\n\nThe paper is well-written and easy to follow. The figures and videos are helpful for understanding this work.\n\nThe framework could be used for co-design problems for both morphology and control or for studying locomotion when the analytical modelling is challenging.\n\nThe integration of a custom LBM-based GPU simulator to resolve dynamics in RL is novel. Furthermore, the combination of geodesic-based-LBS and dynamic correction for internal actuation is novel as far as I understand.\n\nThe evaluation of the approach is rigorous and thorough. The approach outperforms the state of the art for unconventional shapes and performs similarly to the other approaches for the biologically-inspired shapes.\n\nThe approach is shown to generalize to a set of arbitrary morphogenesis, something that prior work struggles with.\n\nWeakness:\nThere is no sim-to-real discussion in this paper or interpretation on why the controllers work. \nThe paper hints the implementation of automated morphology-control co-design, but it does not implement joint optimization. \nThe framework does not yet support general controllers that transfer across unseen shapes, each policy is trained per-shape from scratch. Also, the performance of the controllers under different conditions (eg disturbances) would be another interesting evaluation \nThere is no discussion on the complexity and/or computational cost of the approach, or comparison with state of the art across these lines. This is a big challenge for the field as far as I understand.\n\nQUESTIONS:\nCurrently, each swimmer is trained from scratch, it is unclear whether the trained policy can be generalized or transfer to unseen morphologies. How can this be addressed?\nPlease clarify the computational cost and training speed of the simulator and training method.\nPlease provide more details of each reward functions and how reward weights were chosen. Could a poorly tuned reward limit the emergence of complex locomotion strategies?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel, automated framework for learning control policies for soft-bodied swimmers with arbitrary morphologies. It introduces a unified, reduced-mode control space using geodesic sampling and linear blend skinning to enable morphology-agnostic actuation. A GPU-accelerated simulator based on the Lattice Boltzmann Method is developed to efficiently capture fluid-structure interactions at high fidelity. The method is evaluated across a diverse set of morphologies, including biologically inspired and abstract forms, and demonstrates improved performance over state-of-the-art (SOTA) baselines in terms of swimming effectiveness.\n\nSTRENGTHS AND WEAKNESSES:\nIn general, I found the paper well written and an interesting contribution. In particular:\n\nStrengths:\n- The proposed reduced-mode controller enables efficient and expressive actuation across diverse geometries, reducing the need for expert-designed actuators.\n- The use of LBM for fluid dynamics and a custom GPU-accelerated simulator ensures physically realistic interactions critical for learning effective swimming strategies. The simulator is a good contribution to the research community.\n- The method generalizes to unconventional morphologies where baseline methods fail, showing the strength of a data-driven, morphology-agnostic approach.\n\nWeaknesses:\n- The framework does not seem to optimize or evaluate energy usage. While action magnitudes are regularized, there is no explicit metric  to assess or minimize energy consumption. \n- Claims about the similarity between learned behaviors and evolved biological swimming strategies are based on qualitative visual resemblance only. No quantitative biomechanical comparisons or gait analyses are provided. \n- Despite referencing \"co-design,\" the work only addresses control learning on fixed shapes, and does not co-optimize morphology and control jointly.\n\nIn general, especially the abstract oversells the results in my opinion. For example: \"We showcase how our automated approach discovers high-performance swimming patterns similar to those found in nature over years of evolution.\" Without a more detailed analysis, this claim does not seem to substantiated. Additionally, energy efficiency is a crucial aspect of biological organisms, and doesn't seem considered here.\n\nAdditionally,  the authors write that\"Our work unlocks future opportunities in automated co-design of morphology and control of soft robots in complex hydrodynamic environments.\" Why does this work in particular unlock this possibility and not existing works on soft robots?\n\nQUESTIONS:\n- You state that your method discovers swimming strategies “similar to those found in nature.” How do you measure this similarity? Have you considered comparing swimming trajectories or gait metrics with biological data?\n\n- Does your method account for or optimize the energetic efficiency of swimming? If not, could your framework be extended to incorporate energy-based objectives ?\n\n- Have you considered deploying these controllers on physical soft robots? What challenges do you anticipate in transferring them from simulation to the real world?\n\nLIMITATIONS:\nyes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a new framework for learning control policies for soft-bodied swimming robots with diverse shapes. Unlike traditional approaches that rely on human-designed muscle patterns or simplified fluid simulations, this method uses a low-dimensional, morphology-agnostic control space inspired by how real animals swim. The authors also develop a fast, GPU-based simulator that accurately models the interaction between the swimmer and the surrounding fluid, which is essential for learning realistic swimming strategies.\n\nThe system automatically generates controllers by sampling over reduced deformation modes, removing the need for expert-designed heuristics. Experiments show that the method achieves significantly better performance than existing baselines—especially on unusual shapes like toruses—and can learn advanced behaviors such as exploiting fluid vortices. The results suggest that this framework could support future work in co-designing both the shape and control of soft robots in complex environments.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n\n1. The paper presents a well-integrated framework for soft-body swimming control that combines a GPU-accelerated simulator, a two-way fluid-structure interaction model, and a unified control representation for diverse morphologies. This integration addresses bottlenecks in simulating and learning soft-body locomotion, offering both physical fidelity and computational efficiency.\n\n2. The proposed method demonstrates robust performance not only on biologically-inspired morphologies but also on abstract and unconventional shapes. This highlights the generality and adaptability of the control approach, which does not rely on human-designed heuristics and can discover effective strategies in previously underexplored morphospace.\n\n3. The paper provides high-quality visualizations of the deformation modes, control points, and learned swimming trajectories, which help clarify the system’s behavior and effectiveness. These visual results significantly aid in understanding the qualitative performance of the method.\n\n# Weaknesses:\n\n1. The core contributions primarily lie in the modeling of the soft-body and its interaction with fluid dynamics, while the learning approach itself follows established reinforcement learning method. As such, the work may be better suited to other venues\n\n2. The evaluation largely relies on task-specific rewards that may include regularization or smoothing terms, which can obscure direct assessment of locomotion quality. Inclusion of more interpretable and standardized metrics—such as forward swimming speed, displacement over time, or time-to-target—would provide clearer insight into the quantitative effectiveness of the learned policies.\n\n3. Given the stochastic nature of reinforcement learning, it is important to report training consistency and stability. However, the manuscript does not specify how many training trials were repeated or whether the final performance is robust across seeds. Including training curves and variance across runs would strengthen the empirical evaluation.\n\nQUESTIONS:\n1. What is the simulation frame rate (FPS) achieved for the soft-body fluid-structure simulation?\n\n2. How many training runs were conducted, and how consistent is the performance across different seeds and across the training.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-04"}