# Robots of the Lost Arc: Self-Supervised Learning to Dynamically Manipulate Fixed-Endpoint Cables

arXiv: https://arxiv.org/abs/2011.04840 · HTML: https://mlsanity.com/paper/2011.04840/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2020.

## Built on by (most cited first, 21 of 21)

- [FlowBot3D: Learning 3D Articulation Flow to Manipulate Articulated Objects](https://mlsanity.com/paper/2205.04382/) (2022)
- [Multi-Stage Cable Routing through Hierarchical Imitation Learning](https://mlsanity.com/paper/2307.08927/) (2023)
- [TAX-Pose: Task-Specific Cross-Pose Estimation for Robot Manipulation](https://mlsanity.com/paper/2211.09325/) (2022)
- [That Sounds Right: Auditory Self-Supervision for Dynamic Robot Manipulation](https://mlsanity.com/paper/2210.01116/) (2022)
- [APLA: Additional Perturbation for Latent Noise with Adversarial Training Enables Consistency](https://mlsanity.com/paper/2308.12605/) (2023)
- [SGTM 2.0: Autonomously Untangling Long Cables using Interactive Perception](https://mlsanity.com/paper/2209.13706/) (2022)
- [CHAMP: Conformalized 3D Human Multi-Hypothesis Pose Estimators](https://mlsanity.com/paper/2407.06141/) (2024)
- [DexDLO: Learning Goal-Conditioned Dexterous Policy for Dynamic Manipulation of Deformable Linear Objects](https://mlsanity.com/paper/2312.15204/) (2023)
- [All You Need is LUV: Unsupervised Collection of Labeled Images using Invisible UV Fluorescent Indicators](https://mlsanity.com/paper/2203.04566/) (2022)
- [Learning Correspondence for Deformable Objects](https://mlsanity.com/paper/2405.08996/) (2024)
- [Safe Deep Model-Based Reinforcement Learning with Lyapunov Functions](https://mlsanity.com/paper/2405.16184/) (2024)
- [CUPS: Improving Human Pose-Shape Estimators with Conformalized Deep Uncertainty](https://mlsanity.com/paper/2412.10431/) (2024)
- [A Distributional Treatment of Real2Sim2Real for Object-Centric Agent Adaptation in Vision-Driven Deformable Linear Object Manipulation](https://mlsanity.com/paper/2502.18615/) (2025)
- [H2OFlow: Grounding Human-Object Affordances with 3D Generative Models and Dense Diffused Flows](https://mlsanity.com/paper/2510.21769/) (2025)
- [Dynamic Manipulation of Deformable Objects using Imitation Learning with Adaptation to Hardware Constraints](https://mlsanity.com/paper/2403.12685/) (2024)
- [Towards Improving Learning from Demonstration Algorithms via MCMC Methods](https://mlsanity.com/paper/2405.02243/) (2024)
- [Theoretical Analysis for Expectation-Maximization-Based Multi-Model 3D Registration](https://mlsanity.com/paper/2405.08991/) (2024)
- [Rocket Landing Control with Grid Fins and Path-following using MPC](https://mlsanity.com/paper/2405.16191/) (2024)
- [Posterior-driven Heuristic Support Adaptation in a Probabilistic Treatment of Real2Sim2Real for Vision-Driven Deformable Linear Object Manipulation](https://mlsanity.com/paper/2510.26656/) (2025)
- [Periodic Steady-State Control of a Handkerchief-Spinning Task Using a Parallel Anti-Parallelogram Tendon-driven Wrist](https://mlsanity.com/paper/2604.17863/) (2026)
- [Wiggle and Go! System Identification for Zero-Shot Dynamic Rope Manipulation](https://mlsanity.com/paper/2604.22102/) (2026)

## Abstract (arXiv)

We explore how high-speed robot arm motions can dynamically manipulate cables to vault over obstacles, knock objects from pedestals, and weave between obstacles. In this paper, we propose a self-supervised learning framework that enables a UR5 robot to perform these three tasks. The framework finds a 3D apex point for the robot arm, which, together with a task-specific trajectory function, defines an arcing motion that dynamically manipulates the cable to perform tasks with varying obstacle and target locations. The trajectory function computes minimum-jerk motions that are constrained to remain within joint limits and to travel through the 3D apex point by repeatedly solving quadratic programs to find the shortest and fastest feasible motion. We experiment with 5 physical cables with different thickness and mass and compare performance against two baselines in which a human chooses the apex point. Results suggest that a baseline with a fixed apex across the three tasks achieves respective success rates of 51.7%, 36.7%, and 15.0%, and a baseline with human-specified, task-specific apex points achieves 66.7%, 56.7%, and 15.0% success rate respectively, while the robot using the learned apex point can achieve success rates of 81.7% in vaulting, 65.0% in knocking, and 60.0% in weaving. Code, data, and supplementary materials are available at https: //sites.google.com/berkeley.edu/dynrope/home.