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arxiv: 2605.18617 · v1 · pith:HCO4YX3Znew · submitted 2026-05-18 · 💻 cs.RO · cs.AI· cs.CV

ManiSoft: Towards Vision-Language Manipulation for Soft Continuum Robotics

Ziyu Wei , Luting Wang , Chen Gao , Li Wen , Si Liu This is my paper

Pith reviewed 2026-05-20 09:50 UTC · model grok-4.3

classification 💻 cs.RO cs.AIcs.CV
keywords soft roboticsvision-language manipulationcontinuum robotsrobotic benchmarksoft body simulationreinforcement learningdeformable controltrajectory generation
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The pith

ManiSoft introduces a benchmark for vision-language manipulation using soft continuum robotic arms.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Most vision-language manipulation research targets rigid robotic arms whose fixed shape limits work in cluttered or confined spaces. Soft arms can deform to reach such areas but introduce unreliable proprioception and distributed actuation challenges. The paper presents ManiSoft as a benchmark featuring a tailored simulator that models soft-body dynamics and contact interactions through an elastic force constraint, along with four tasks that stress different aspects of deformable control. An automated pipeline creates 6,300 scenes and expert trajectories by combining a high-level planner that sets waypoints with a low-level reinforcement learning policy that outputs torques. Tests of three policy models succeed in clean scenes yet degrade sharply under randomization, mainly from visual errors in estimating arm state and underuse of bending for obstacle avoidance.

Core claim

ManiSoft is a benchmark for vision-language manipulation for soft continuum robotics. It features a tailored simulator that couples realistic soft-body dynamics with contact-rich interactions via an elastic force constraint. On this basis, ManiSoft defines four tasks, each highlighting distinct aspects of deformable control, from basic end-effector coordination to obstacle avoidance. To support policy training and evaluation, ManiSoft includes an automated pipeline that generates 6,300 diverse scenes and corresponding expert trajectories. To produce high-quality trajectories at scale, a high-level planner decomposes each task into a sequence of waypoints, followed by a low-level RL policy to

What carries the argument

The tailored simulator that couples realistic soft-body dynamics with contact-rich interactions via an elastic force constraint, together with the high-level planner and low-level RL policy for expert trajectory generation.

If this is right

  • The four tasks allow systematic testing of basic coordination through advanced obstacle avoidance in deformable systems.
  • The pipeline for generating 6,300 scenes and trajectories supports scalable training and evaluation of vision-language policies.
  • Benchmark results identify concrete failure modes in visual proprioception and deformability exploitation that new methods must solve.
  • ManiSoft can function as a shared testbed that transfers techniques from rigid-arm research to soft-arm settings.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Better vision models focused on estimating continuous arm shape could narrow the performance gap seen under randomization.
  • Policies that explicitly plan bending sequences around obstacles might make fuller use of soft-arm deformability.
  • Transferring ManiSoft-trained policies to hardware soft robots would test whether the elastic-force simulator captures real dynamics.
  • The benchmark could extend to other soft-robot domains such as navigation or inspection in tight environments.

Load-bearing premise

The tailored simulator accurately couples realistic soft-body dynamics with contact-rich interactions via an elastic force constraint, and the high-level planner plus low-level RL policy produces high-quality expert trajectories suitable for policy training and evaluation.

What would settle it

If policies trained on ManiSoft trajectories show no performance advantage over rigid-arm methods when tested on physical soft robots in confined spaces, the benchmark would fail to demonstrate a useful bridge between the two domains.

Figures

Figures reproduced from arXiv: 2605.18617 by Chen Gao, Li Wen, Luting Wang, Si Liu, Ziyu Wei.

Figure 1
Figure 1. Figure 1: (a) Rigid arms operate in a low-dimensional action space and can fail due to limited shape adaptation, whereas soft arms driven by distributed low-level actuation can continuously deform to reach around obstacles. (b) Example expert trajectories for the four ManiSoft tasks. (c) The data generation pipeline comprises an asset library, clean and randomized scene generation, hierarchical trajectory generation… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the soft arm modeling in our Simulator. The soft body is modeled as a Cosserat rod that moves under the influence of an external torque τ e. Interaction between soft body and EEF is represented via an an elastic force constraint. Relative displacement ∆x or relative rotation ∆θ between them induces corresponding restoring forces and torques. et al., 2023). To address the challenges of modeling … view at source ↗
Figure 3
Figure 3. Figure 3: Scene generation in ManiSoft. Objects are sampled from the asset library to create a clean scene, and randomized scenes are generated by injecting objects as obstacles and varying surface textures. Instructions are produced with the descriptions of relevant objects. In the randomized setting, diverse descriptions are leveraged to enhance the linguistic richness. perception, spatial reasoning, and obstacle … view at source ↗
Figure 4
Figure 4. Figure 4: Trajectory generation pipeline in ManiSoft. (a) An executor is trained via RL policy to transform waypoint (6-DoF pose) into torques. (b) RL rewards are designed to balance accuracy and stability, consisting of a pose difference reward Rd negatively correlated with the pose difference, and a stability reward Rs that penalizes or rewards changes in pose difference. (c) Task-specific rules are predefined to … view at source ↗
Figure 5
Figure 5. Figure 5: Statistical analysis of the ManiSoft Benchmark. (a) Distribution of trajectory lengths. Tasks in ManiSoft generally involve long trajectories, with the STK task exhibiting notably longer trajectories than the others. (b) Frequency distribution of target object categories, highlighting the diversity of manipulable objects in ManiSoft. (c) Spatial distribution of initial target object positions on the tablet… view at source ↗
Figure 6
Figure 6. Figure 6: Visualization of executor trained w/ and w/o the sta￾bility reward Rs. (Left) The pose difference between the end￾effector and the target pose over time. (Right) The soft robotic arm’s trajectory shadows during the final 1000 simulation steps. The red line represents the soft body, and the blue circle indicates the end-effector. average of 40 language instructions per scene. The dataset is split into train… view at source ↗
Figure 7
Figure 7. Figure 7: Visualization of the ManiSoft. The left example is a clean scene, while the right is a randomized scene [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: Comparison of DP and OpenVLA-OFT on the same task: (a) DP successfully completes the task; (b) OpenVLA-OFT exhibits the “stop-moving” behavior. drifts laterally and ultimately fails to reach the target object. Challenges in leveraging soft arm compliance. Com￾pared to rigid arms, soft arms offer advantages in flexibil￾ity, allowing them to adapt their shape to the environment and reach behind obstacles. Ho… view at source ↗
Figure 10
Figure 10. Figure 10: Visualization of the trained executor controlling the soft robotic arm to move to the target pose. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Visualization of COLL Task. The first two are for the clean setting, and the last two are for the randomized setting. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Visualization of ALN Task. The first two are for the clean setting, and the last two are for the randomized setting. Instruction: Arrange the tableware in stacks sorted by size. 𝑡 = 1 𝑡 = 2 𝑡 = 3 𝑡 = 4 𝑡 = 5 𝑡 = 6 𝑡 = 7 𝑡 = 8 Instruction: Pile up the eating utensils by size [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Visualization of STK Task. The first one is for the clean setting, and the last one is for the randomized setting. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Visualization of ARR Task. The first two are for the clean setting, and the last two are for the randomized setting. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_14.png] view at source ↗
read the original abstract

Most existing vision-language manipulation research targets rigid robotic arms, whose fixed morphology limits adaptability in cluttered or confined spaces. Soft robotic arms offer an appealing alternative due to their deformability, but confront challenges such as unreliable proprioception and distributed low-level actuation. To investigate these challenges, we introduce \ManiSoft, a benchmark for vision-language manipulation with soft arms. ManiSoft features a tailored simulator that couples realistic soft-body dynamics with contact-rich interactions via an elastic force constraint. On this basis, ManiSoft defines four tasks, each highlighting distinct aspects of deformable control, from basic end-effector coordination to obstacle avoidance. To support policy training and evaluation, \ManiSoft{} includes an automated pipeline that generates $6{,}300$ diverse scenes and corresponding expert trajectories. To produce high-quality trajectories at scale, we first employ a high-level planner to decompose each task into a sequence of waypoints, followed by a low-level reinforcement learning policy that generates torque commands to track waypoints. Benchmarking three representative policy models shows relatively promising results in clean scenes but substantial performance drop under randomization. Visualization analysis indicates that failures stem primarily from inaccurate visual estimation of proprioceptive state and limited exploitation of deformability for adaptive obstacle avoiding. We anticipate ManiSoft to serve as a valuable testbed, bridging the gap between rigid and soft arms in the context of vision-language manipulation. Out codes and datasets are released at https://buaa-colalab.github.io/ManiSoft.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces ManiSoft, a benchmark for vision-language manipulation with soft continuum robotic arms. It features a tailored simulator coupling soft-body dynamics and contact-rich interactions via an elastic force constraint, defines four tasks highlighting deformable control aspects, generates 6300 diverse scenes with expert trajectories via a high-level planner followed by low-level RL policy for torque commands, and benchmarks three representative policy models that show promising results in clean scenes but substantial performance drops under randomization, with failures attributed primarily to inaccurate visual proprioception and limited exploitation of deformability.

Significance. If the simulator's dynamics prove representative of real soft continuum robots, ManiSoft could serve as a useful testbed bridging rigid and soft arm research in vision-language manipulation. The automated pipeline for scene and trajectory generation at scale, along with released code and datasets, supports reproducibility and community use. The empirical focus on challenges like unreliable proprioception and distributed actuation in soft arms addresses a genuine gap, though the absence of detailed quantitative validation metrics limits immediate impact assessment.

major comments (3)
  1. [Simulator section] Simulator description (likely §3 or equivalent): the claim that the tailored simulator 'couples realistic soft-body dynamics with contact-rich interactions via an elastic force constraint' lacks quantitative validation against Cosserat rod models, finite-element references, or physical soft-arm data for metrics such as tip position error, curvature profiles, or force under distributed actuation; this is load-bearing for the central claim that ManiSoft is a representative testbed.
  2. [Benchmarking results] Benchmarking and results section: the reported 'substantial performance drop under randomization' and attribution to 'inaccurate visual estimation of proprioceptive state' are presented without supporting data tables, ablation studies, or error analysis (e.g., success rates, proprioception error distributions), undermining assessment of whether the drop reflects soft-robot challenges or simulator limitations.
  3. [Expert trajectory generation] Trajectory generation pipeline: the high-level planner plus low-level RL policy is asserted to produce 'high-quality expert trajectories' suitable for policy training, but no quantitative metrics on waypoint tracking accuracy, trajectory smoothness, or success rates across the 6300 scenes are provided to substantiate this for downstream evaluation.
minor comments (2)
  1. [Abstract] Abstract: 'Out codes and datasets' should be corrected to 'Our codes and datasets'.
  2. [Figures and notation] Notation and figures: ensure consistent use of task names and that visualizations of failure cases clearly label proprioception errors versus deformability exploitation.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thoughtful and constructive comments. We address each major comment point by point below, indicating the revisions we will make to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Simulator section] Simulator description (likely §3 or equivalent): the claim that the tailored simulator 'couples realistic soft-body dynamics with contact-rich interactions via an elastic force constraint' lacks quantitative validation against Cosserat rod models, finite-element references, or physical soft-arm data for metrics such as tip position error, curvature profiles, or force under distributed actuation; this is load-bearing for the central claim that ManiSoft is a representative testbed.

    Authors: We agree that quantitative validation would strengthen the simulator's credibility as a representative testbed. In the revised manuscript, we will add a dedicated subsection with direct comparisons to Cosserat rod models and finite-element simulations, reporting metrics such as tip position error and curvature profiles under distributed actuation. For physical soft-arm data, our work is currently simulation-focused; we will explicitly note this scope limitation and discuss it as an avenue for future validation. revision: yes

  2. Referee: [Benchmarking results] Benchmarking and results section: the reported 'substantial performance drop under randomization' and attribution to 'inaccurate visual estimation of proprioceptive state' are presented without supporting data tables, ablation studies, or error analysis (e.g., success rates, proprioception error distributions), undermining assessment of whether the drop reflects soft-robot challenges or simulator limitations.

    Authors: We acknowledge that additional empirical support is needed to clarify the sources of performance drops. We will incorporate new tables reporting success rates in clean versus randomized scenes, proprioception error distributions, and ablation studies isolating the contributions of visual estimation inaccuracies and randomization effects in the revised results section. revision: yes

  3. Referee: [Expert trajectory generation] Trajectory generation pipeline: the high-level planner plus low-level RL policy is asserted to produce 'high-quality expert trajectories' suitable for policy training, but no quantitative metrics on waypoint tracking accuracy, trajectory smoothness, or success rates across the 6300 scenes are provided to substantiate this for downstream evaluation.

    Authors: We will expand the trajectory generation section to include quantitative metrics, specifically average waypoint tracking error, trajectory smoothness measures (such as integrated jerk), and overall success rates of the expert policy across all 6300 scenes. These additions will be placed in the main text or supplementary material to better substantiate trajectory quality. revision: yes

Circularity Check

0 steps flagged

Empirical benchmark introduction with no derivation chain

full rationale

The paper introduces ManiSoft as an empirical benchmark for vision-language manipulation on soft continuum robots. It describes a tailored simulator, four tasks, and an automated pipeline that generates scenes and expert trajectories using a high-level planner followed by low-level RL. No mathematical derivations, first-principles predictions, or equations are present that could reduce by construction to fitted inputs or self-referential definitions. Claims rest on the described methodology and released code/data rather than any load-bearing self-citation chain or ansatz smuggling. This is a standard empirical contribution whose central value (the benchmark itself) does not rely on circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based on abstract only; no explicit free parameters, axioms, or invented entities are detailed beyond the introduction of a custom simulator and elastic force constraint whose tuning details are not provided.

pith-pipeline@v0.9.0 · 5798 in / 1057 out tokens · 68611 ms · 2026-05-20T09:50:29.047273+00:00 · methodology

discussion (0)

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Reference graph

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    ACC(%) #Steps ACC(%) #Steps ACC(%) #Steps ACC(%) #Steps ACC(%) #Steps ACC(%) #Steps Bottle70.0543 15.044450.0 530 5.0 749 0.0 -45.0 569 Pill Bottle75.0545 10.042055.0 520 10.0 349 5.0113 40.0599 Can100.0 53535.0 550 60.0 597 10.05150.0 -45.0663 Cup65.0502 15.044260.0 529 5.0 603 5.0516 30.0591 Sand Clock35.0538 15.0 58835.0 4730.0 - 0.0 -15.0 454 Shoe35.0...

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    2.53712.52780.0 - 1.7324 RDT (Liu et al., 2025b) 0.0 - 7.5 498 5.02604.2 379 OpenVLA-OFT (Kim et al., 2025)5.055920.043055.048026.7489 𝑧 𝑥 𝑦 𝑧 𝑥 𝑦 𝑧 𝑥 𝑦 𝑧 𝑥 𝑦 𝑧 𝑥 𝑦 𝑧 𝑥 𝑦 𝑧 𝑥 𝑦 𝑧 𝑥 𝑦 𝑧 𝑥 𝑦 𝑧 𝑥 𝑦 𝑧 𝑥 𝑦 𝑧 𝑥 𝑦 𝑡 = 1 𝑡 = 2 𝑡 = 3 𝑡 = 4 𝑡 = 5 𝑡 = 6 Figure 10.Visualization of the trained executor controlling the soft robotic arm to move to the target pose. 16 Ma...

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