Learned simulators for deformable linear objects (DLOs) such as cables have to predict the motion of cables they were not trained on and stay stable over long rollouts. Most of their error occurs where the cable touches itself or the floor. Attention over all pairs of cable segments can represent contact between parts of the cable that are far apart along its length, but attention has no notion of geometry. A cable has two pairwise distances, which agree only while it is straight: the arc-length distance along the cable, which governs elastic forces, and the Euclidean distance in space, which governs contact. We add a physical attention bias, an additive term on the attention logits with a learned rate, and ask which distance it should use. We compare no bias, each distance alone, and both distances on disjoint sets of heads, keeping the rest of the model and the training protocol fixed. A physical bias improves prediction on unseen cables. The gain is largest when attention is the only mechanism that connects distant segments: there, the arc-length bias reduces prediction error by 15% and more than halves the drift in segment length. The Euclidean bias alone stays close to unbiased attention, while assigning both distances across heads is best or near-best on every metric we report. Code and per-run records: https://github.com/avihaig/dlogps.
Figures & tables
relℓ2 (frac. of L )
δ (frac. of ℓ0 )
v (frac. of frames)
local stream on
Unbiased
0.0325±0.0281±0.0058
0.0300±0.0159±0.0032
0.171±0.231±0.009
Euclidean
0.0320±0.0267±0.0042
0.0303±0.0135±0.0021
0.174±0.229±0.032
Chain
0.0316±0.0341±0.0057
0.0271±0.0124±0.0023
0.167±0.227±0.030
Mixed
0.0302±0.0251±0.0067
0.0267±0.0109±0.0030
0.168±0.229±0.012
Euclidean+Chain only
0.0297±0.0288±0.0035
0.0275±0.0129±0.0007
0.165±0.229±0.012
Table 1: Unseen-cable test, 400 windows of 400 -frame rollouts; mean of seed means ± mean of window sds ± sd of seed means; bold: lowest mean per block.
Appendix figures & tables1 asset
Supplementary material from the paper’s appendix.
Appendix
floor contact relℓ2
free flight relℓ2
Unbiased
0.0511±0.0536±0.0105
0.0062±0.0095±0.0001
Euclidean
0.0510±0.0615±0.0080
0.0062±0.0099±0.0005
Chain
0.0529±0.0871±0.0122
0.0064±0.0131±0.0014
Mixed
0.0484±0.0551±0.0135
0.0058±0.0097±0.0011
Euclidean+Chain only
0.0479±0.0643±0.0068
0.0061±0.0127±0.0010
Appendix
Table 2: Local-on rows of tab:aggregate split by phase of the recorded trajectory ( 400 and 345 eligible windows); bold: lowest mean per column. Every column ordering here sits inside the seed spread.
Linear-deformable manipulation remains challenging due to the complex deformations of objects such as cables and ropes. Prior data-driven approaches, particularly imitation learning, have shown some promise in narrowly defined settings but typically require thousands of demonstrations for specific tasks and cable types, limiting scalability and generalization. We introduce a sim-to-real reinforcement learning (RL) framework for multi-stage cable routing that leverages GPU-parallelized simulation to approximate linear deformable behaviors. Training across thousands of parallel simulations enables the learned policies to generalize across diverse cable geometries and deformation patterns. To bridge the sim-to-real gap, we propose a novel deployment strategy that combines a Simulation In the LOop (SILO) execution framework, localized RL policies, and robust cable state estimation. On real-world cable routing tasks, our approach achieves higher success rates and 2x reduction in cycle times compared to prior state-of-the-art learning methods. To our knowledge, this is the first successful sim-to-real transfer of RL policies for multi-stage cable routing. Videos and additional visualizations are available at https://silo-cable-routing.github.io/
Stone Tao, Jie Xu, Hesam Rabeti +3
1NVIDIA Corporation · University of California, San Diego
The manipulation of Deformable Linear Objects (DLOs) such as cables poses a significant challenge for automation due to their infinite degrees of freedom and non-linear dynamics. In this paper we present a machine learning based optimal control approach for the manipulation of DLOs. This approach is divided into two main components: modeling and control. For modeling the dynamics of the DLO, we propose a learning based approach using a bidirectional Long Short-Term Memory (biLSTM) network. The biLSTM network is trained on synthetic data generated by the MuJoCo physics engine. For manipulating the DLO, a model predictive control strategy that employs Model Predictive Path Integral (MPPI) control is selected. The proposed approach is evaluated through simulation and experiments. The results demonstrate the effectiveness of the proposed method in achieving accurate and efficient manipulation of DLOs.
Lukas Zeh, Johannes Meiwaldt, Zexu Zhou +2
Institute for Control Engineering of Machine Tools and Manufacturing Units, University of Stuttgart, Stuttgart, Germany
Cable routing requires coordinated control of global cable topology and changing local contacts. We present CableVLA, an end-to-end multimodal vision-language-action framework that converts simulation-privileged supervision into deployable cable-topology and tactile representations. TopoHead distills node-level physics and current and future cable-topology information into causal visual context for the action expert. TacSense uses complementary frame and taxel branches to learn contact dynamics from resistive arrays, with simulator-derived kinematics and contact events providing supervision beyond the measured force map. A contact gate activates force-tactile residuals that refine the next 8 arm-and-gripper actions of a frozen topology-conditioned policy. Across 345 MuJoCo evaluations, CableVLA improves success from 62.6% for the π0.5-V visual baseline to 84.9%. TacSense achieves pronounced gains in slip-transition recognition over a CNN-LSTM baseline with a similar parameter count, and this advantage persists under frozen-encoder probes. Topology prediction and 57-task tactile evaluations assess representation quality, while policy adaptation studies evaluate downstream control performance. Cross-simulator and real-robot comparisons further examine zero-shot policy transfer under changes in dynamics and sensing.
Zhifei Teng, Bo Feng, Xiang Zou +4
State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China. · School of Mathematics, Harbin Institute of Technology, Harbin, China.