SoRoMoX: Fast, Differentiable, and Parallelizable Soft Robot Models

• URL: http://arxiv.org/abs/2608.06650v1
• Date: Thu, 06 Aug 2026 23:31:47 GMT
• Title: SoRoMoX: Fast, Differentiable, and Parallelizable Soft Robot Models
• Abstract summary: SoRoMoX implements articulated, Piecewise Constant Strain, and Variable Strain models through a unified, control-ready interface.<n> Sequential CPU rollouts are up to 18.1x faster than state-of-the-art alternatives, while GPU-parallel rollouts increase throughput by up to 234.6x.
• Score: 40.64972837908431
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reduced-order models based on Cosserat-rod theory are now well established, and modeling theory is no longer the primary bottleneck in soft-robot control. Their implementations, however, do not support the differentiable, GPU-parallel, and control-oriented workflows that underpin advanced rigid-robotics applications. Here, we fill this gap with SoRoMoX (Soft Robot Models in JAX), a fully numerical, JIT-compilable Python/JAX framework. SoRoMoX implements articulated, Piecewise Constant Strain, and Variable Strain models through a unified, control-ready interface that provides inertia matrices, gravitational and elastic forces, Jacobians, and their derivatives. To our knowledge, it is the first rod/strain-based soft-robot modeling framework that runs directly on GPUs and is end-to-end differentiable with respect to states, inputs, and parameters. Sequential CPU rollouts are up to 18.1x faster than state-of-the-art alternatives, while GPU-parallel rollouts increase throughput by up to 234.6x. This performance enables workflows that were previously impractical or impossible: static-equilibrium system identification with 66% lower marker RMSE; residual-force learning with a further 64% reduction; computed-torque tracking with RMSE reduced by a factor of approximately 500 relative to model-free PD; control-gain optimization with up to 62% lower loss than untuned gains; safety-constrained control using high-order control barrier functions to keep the peak contact force within a prescribed 5 N bound, compared with 33.5 N without the safety constraint; and reinforcement-learning policy training up to 7x faster than a CPU PyElastica discrete-rod baseline through massively parallel rollouts.

Related papers

• On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training Stability [57.03393882933515]
  We describe the architecture and ablations of Qwen3.8-Flash-Next, a sparse mixture-of-experts model with 125B parameters, 6B activated per token, and 51B parameters of n-gram embedding tables held off the accelerator.<n>On fourteen pre-training benchmarks the model leads the 397B-A17B predecessor on eight and trails it on the rest by at most 2.6 points, at 1/3 the activated parameters, 1/3 the training tokens, and roughly 1/9 the training FLOPs.
  arXiv  Detail & Related papers  (2026-08-31T06:35:07Z)
• Optimization Algorithms for Joint OFDM Waveform Design and RIS Configuration in 6G Networks: From Convex Relaxation to Foundation Models [1.9290392443571385]
  Seventy-eight joint OFDMRIS optimization works published between 2021 and 2026 are surveyed.<n>No standardized benchmark exists, and cross-paper comparisons remain infeasible.<n>Six open challenges emerge from synthesis-based benchmark deployment.
  arXiv  Detail & Related papers  (2026-06-30T08:34:46Z)
• LocalNav: Distilling Frontier VLMs and Embodied RL for On-Device Object Goal Navigation [53.88286427894783]
  Vision Language Models (VLMs) have emerged in the robotic domain as a powerful tool that enables environmental perception with language context.<n>Yet, their computational footprint typically confines them to cloud execution, hindering low-latency inference with local deployment on resource-constrained robots.<n>We present a distillation strategy that transfers complex spatial-semantic reasoning from large frontier models into a lightweight, 4B- parameter local VLM for edge execution on embedded GPU devices.
  arXiv  Detail & Related papers  (2026-06-26T09:11:59Z)
• Rollback-Free Stable Brick Structures Generation [51.856925926010184]
  Existing approaches rely on external physical simulators during inference to perform rejection sampling and brick-by-brick rollbacks.<n>We propose a reinforcement learning paradigm that shifts physical validity enforcement from test-time correction to training-time policy optimization.<n> Experimental results demonstrate that our approach achieves state-of-the-art generation quality while accelerating inference speed by orders of magnitude.
  arXiv  Detail & Related papers  (2026-05-07T21:06:44Z)
• cuRoboV2: Dynamics-Aware Motion Generation with Depth-Fused Distance Fields for High-DoF Robots [12.825461775215366]
  cuRoboV2 is a motion generation stack that scales from single-arm manipulators to full humanoids.<n>We present three key innovations: (1) B-spline trajectory optimization that enforces smoothness and torque limits; (2) a GPU-native TS/ESDF perception pipeline that generates dense signed distance fields covering the full workspace; and (3) differentiable inverse dynamics map-reduce self-native that achieves up to 61x speedup while also extending to high-DoF humanoids.
  arXiv  Detail & Related papers  (2026-03-05T18:58:04Z)
• MiniCPM-SALA: Hybridizing Sparse and Linear Attention for Efficient Long-Context Modeling [80.48332380100915]
  MiniCPM-SALA is a hybrid model that integrates the high-fidelity long-context modeling of sparse attention with the global efficiency of linear attention.<n>On a single NVIDIA A6000D GPU, the model achieves up to 3.5x the inference speed of the full-attention model at the sequence length of 256K tokens.
  arXiv  Detail & Related papers  (2026-02-12T09:37:05Z)
• Efficient Multi-Model Orchestration for Self-Hosted Large Language Models [2.3275796286410677]
  Pick and Spin is a framework that makes self-hosted orchestration and economical.<n>It integrates a unified Helm-based deployment system, adaptive scale-to-zero automation, and a hybrid routing module.<n>It achieves up to 21.6% higher success rates, 30% lower latency, and 33% lower cost per query compared with static deployments of the same models.
  arXiv  Detail & Related papers  (2025-12-26T22:42:40Z)
• GraphPerf-RT: A Graph-Driven Performance Model for Hardware-Aware Scheduling of OpenMP Codes [7.204137900029132]
  We introduce GraphPerf-RT, the first surrogate that unifies task DAG topology, CFG-derived code semantics, and runtime context.<n>We validate GraphPerf-RT on three embedded ARM platforms (Jetson TX2, Jetson Orin NX, RUBIK Pi)<n>Experiments show that GraphPerf-RT achieves 66% makespan reduction (0.97 +/- 0.35s) and 82% energy reduction (0.006 +/- 0.005J) compared to model-free baselines.
  arXiv  Detail & Related papers  (2025-12-12T23:46:05Z)
• Adaptive Model-Predictive Control of a Soft Continuum Robot Using a Physics-Informed Neural Network Based on Cosserat Rod Theory [2.7114475063296455]
  This work introduces a real-time-capable nonlinear model-predictive control (MPC) framework for SCRs based on a domain-decoupled physics-informed neural network (DD-PINN)<n>In simulation, it demonstrates accurate tracking of dynamic trajectories and setpoint control with end-effector position errors below 3 mm.<n>In real-world experiments, the controller achieves similar accuracy and accelerations up to 3.55 m/s2.
  arXiv  Detail & Related papers  (2025-08-18T07:24:36Z)
• FlexiGPT: Pruning and Extending Large Language Models with Low-Rank Weight Sharing [59.12511498024836]
  We present a method to prune large language models (LLMs) that selectively prunes model blocks based on an importance score.<n>We propose a principled metric to replace each pruned block using a weight-sharing mechanism.<n> Empirical evaluations demonstrate substantial performance gains over existing methods.
  arXiv  Detail & Related papers  (2025-01-24T18:46:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.