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#manipulation

5 curated events
papersTODAY 04:00 UTC

Proprioception-Anchored Cross-Modal Pretraining for Zero-Shot Sim-to-Real Assembly

The paper introduces a pretraining method that uses proprioceptive signals as an anchor to align different sensory modalities, aiming to help robots handle contact-rich assembly tasks. Such tasks demand submillimeter precision and accurate force interpretation during sustained contact, which makes sim-to-real transfer difficult. The approach targets zero-shot deployment of policies trained in simulation onto real hardware. This is a replacement listing on arXiv cs.AI.

robotics/sim-to-real/pretraining/manipulation
papersSEP 10 04:00 UTC

TaMeSo-bot Combines Tactile Memory and Soft Wrist for Robust Object Insertion

Researchers have introduced TaMeSo-bot, a soft robotic system that stores and retrieves touch-based experience to support contact-rich manipulation tasks. By pairing a masked encoding approach with a compliant soft wrist, the system aims to handle uncertainty in tasks such as inserting keys or other objects. The paper is available on arXiv with cross-listings in the AI and machine learning categories.

robotics/soft-robotics/tactile-sensing/manipulation
papersSEP 10 04:00 UTC

Probabilistic Real2Sim2Real approach improves vision-driven deformable linear object manipulation

A new research paper applies likelihood-free inference to real2sim2real transfer for manipulating deformable linear objects such as cables using vision. By estimating a distribution over simulation parameters from black-box models, the method handles nonlinear and stochastic dynamics that are hard to model directly. A posterior-driven heuristic then adapts the inferred parameter support so control policies can generalize to varied deployment conditions.

robotics/simulation/inference/manipulation
papersSEP 11 04:00 UTC

Zero-shot rope manipulation framework combines safe wiggle action with system identification

Researchers propose "Wiggle and Go!", a two-stage method that lets a robot manipulate rope without prior training. A short, low-risk wiggling motion gathers data about the rope's dynamics, which is then used to plan a reliable dynamic throw. The approach targets tasks where a single error causes unrecoverable failure.

robotics/zero-shot/system-identification/manipulation