Robust Sim-to-Real Cloth Untangling through Reduced-Resolution Observations via Adaptive Force-Difference Quantization

Abstract

Robotic cloth untangling requires progressively disentangling fabric by adapting pulling actions to changing contact and tension conditions. Because large-scale real-world training is impractical due to cloth damage and hardware wear, sim-to-real policy transfer is a promising solution. However, cloth manipulation is highly sensitive to interaction dynamics, and policies that depend on precise force magnitudes often fail after transfer because similar force responses cannot be reproduced due to the reality gap. We observe that untangling is largely characterized by qualitative tension transitions rather than exact force values. This indicates that directly minimizing the sim-to-real gap in raw force measurements does not necessarily align with the task structure. We therefore hypothesize that emphasizing coarse force-change patterns while suppressing fine environment-dependent variations can improve robustness of sim-to-real transfer. Based on this insight, we propose Adaptive Force-Difference Quantization (ADQ), which reduces observation resolution by representing force inputs as discretized temporal differences and learning state-dependent quantization thresholds adaptively. This representation mitigates overfitting to environment-specific force characteristics and facilitates direct sim-to-real transfer. Experiments in both simulation and real-world cloth untangling demonstrate that ADQ achieves higher success rates and exhibits greater robustness in sim-to-real transfer than policies using raw force inputs. Supplementary video is available at https://youtu.be/ZeoBs-t0AWc

Figures (10)

• Figure 1: Overview of sim-to-real policy transfer for cloth untangling. In simulation, only the entangled portion of the cloth is modeled, and the policy is trained to progressively resolve the entanglement by fixing one hand (pin hand) and pulling with the other (pull hand). Then, the learned policy is transferred to the real world.
• Figure 2: Overview of ADQ. Raw force measurements are converted into force differences and discretized into three states (increase: $+1$, decrease: $-1$, no change: $0$). The quantization thresholds are treated as policy-controlled variables, enabling adaptive sensitivity during execution. The resulting quantized force-difference representation is fed back into the policy to determine both the pulling actions and the thresholds. Policy training is performed within a reinforcement learning framework using this observation design.
• Figure 3: Local entanglement model for simulating cloth entangling. The entangled cloth portion is approximated as a chain of capsule segments connected by spherical joints where neighboring segments overlap to form a continuous body. To represent self-contact without introducing unstable persistent contacts, collisions are disabled for adjacent (overlapping) pairs and enabled only for non-neighboring pairs.
• Figure 4: Sim-to-Sim performance comparison in Gazebo. Lower (more negative) writhe reduction indicates better untangling. Statistical significance is assessed using one-sided Welch's t-tests (Ours vs. each baseline; alternative: Ours is better), with Holm correction for multiple comparisons. * indicates significance level of $p<0.05$. Each method was evaluated over $n{=}10$ trials.
• Figure 5: Snapshots of learned policy execution in the Gazebo simulator. The initial scene before policy execution and the final scene after execution are shown. An overview of the developed Gazebo-based cloth untangling environment is illustrated on the right side.