arXiv:2608.29601v2 Announce Type: replace-cross Abstract: We present $N_0$-Foundation, a paradigm for tactile-enabled embodied manipulation, which integrates tactile sensing hardware, large-scale mul...
arXiv:2609.34182v2 Announce Type: replace-cross Abstract: Dexterous manipulation requires tactile feedback. However, robot tactile demonstrations are difficult to scale,because dexterous-hand teleope...
arXiv:2609.15726v1 Announce Type: cross Abstract: Tactile sensing provides contact information that can be difficult to infer from vision alone, but tactile hardware for dexterous hands has not conve...
arXiv:2610.10288v1 Announce Type: new Abstract: Large-scale egocentric human interaction data is becoming an important source of physical supervision for embodied learning, yet video alone leaves the...
TACROSS is a low‑cost, scalable system that enables robots to learn from human touch by aligning tactile streams at the level of contact events rather than raw sensor values. It combines a $10.86 piezoresistive glove with 285 sensing points and a temporal Transformer that maps heterogeneous signals into a shared 256‑dimensional latent space. The approach uses robot demonstrations for action supervision while human demonstrations aid tactile representation learning, achieving a 3.5‑fold efficiency improvement and a 95.7% reduction in demonstration equipment cost across four manipulation tasks.
arXiv:2609.21449v1 Announce Type: new Abstract: World Action Models bring the predictive capabilities of video models into robot action generation, providing a rich foundation for modeling future vis...
DreamTacVLA is a Vision‑Language‑Action framework that incorporates high‑resolution tactile images alongside wrist‑camera and third‑person vision to enable robots to anticipate and react to contact dynamics. It uses a Hierarchical Spatial Alignment loss to align tactile tokens with visual counterparts and a tactile world model to predict future tactile signals, trained on a hybrid dataset from digital twins and real experiments. The approach achieves up to 95% success on contact‑rich manipulation tasks, outperforming existing VLA baselines.
DexTouch-WM is an action‑conditioned world model that learns from scalable human touch to predict future RGB observations and bilateral tactile dynamics for dexterous robot manipulation. By using compatible piezoresistive arrays on both human and robot hands and retargeting human motion into the robot action space, the model can be supervised with human interaction data while keeping a fixed amount of real‑robot supervision. Experiments show that adding up to 100 hours of human interaction improves robot‑domain visual, geometric, and contact prediction, and the model can serve as a surrogate environment for policy evaluation and synthetic trajectory generation.
SoTa is a low‑cost, capacitive tactile skin that offers full‑hand coverage for both humans and robots, featuring a shared layout of 202 taxels across finger and palm regions. The multilayer design with fabric electrodes allows in‑house fabrication for under $10 in materials, and the sensor maintains over 97% of its response after 10,000 loading‑unloading cycles and 1,280 tight‑fist folds. Using SoTa, human‑robot co‑training improves in‑distribution success on three contact‑rich manipulation tasks, and adding human demonstrations more than doubles mean success across eight evaluation conditions, from 22.8% to 45.9%, including all out‑of‑distribution scenarios.
arXiv:2606. 11637v1 Announce Type: new Abstract: Touch is a key modality for embodied agents to understand the physical world.
arXiv:2609.09119v1 Announce Type: cross Abstract: Dexterous manipulation involves contact-rich and fine-grained interactions with the physical world, posing significant challenges for existing vision...
DeCAL is a vision‑language‑action model designed for dexterous manipulation that incorporates tactile sensing through adaptive visuo‑tactile fusion and latent co‑imagination. It uses a Mixture‑of‑Transformers architecture with specialized experts for understanding, imagination, and action, enabling efficient information flow and dynamic regulation of tactile inputs. Experiments show DeCAL achieves state‑of‑the‑art performance, with a 71% average success rate and 83.4% progress success rate, and generalizes well to unseen scenarios.