Sharfin Islam

Sharfin Islam, Zewen Chen, Zhanpeng He et al.

Bimanual robot manipulators can achieve impressive dexterity, but typically rely on two full six- or seven- degree-of-freedom arms so that paired grippers can coordinate effectively. This traditional framework increases system complexity while only exploiting a fraction of the overall workspace for dexterous interaction. We introduce the MiniBEE (Miniature Bimanual End-effector), a compact system in which two reduced-mobility arms (3+ DOF each) are coupled into a kinematic chain that preserves full relative positioning between grippers. To guide our design, we formulate a kinematic dexterity metric that enlarges the dexterous workspace while keeping the mechanism lightweight and wearable. The resulting system supports two complementary modes: (i) wearable kinesthetic data collection with self-tracked gripper poses, and (ii) deployment on a standard robot arm, extending dexterity across its entire workspace. We present kinematic analysis and design optimization methods for maximizing dexterous range, and demonstrate an end-to-end pipeline in which wearable demonstrations train imitation learning policies that perform robust, real-world bimanual manipulation.

Zinan Li, Yiyang Ling, Yuming Gu et al.

The sense of touch is central to manipulation, especially when vision is occluded or ambiguous. Although combining vision and touch improves manipulation, learning robust visuo-tactile policies requires substantial tactile data. Such data remains scarcer than visual data, because tactile sensors are fragile, specialized, and hard to standardize. To address this, we present Feature-Extracted Latent Tactile (FELT), a learning-based framework that synthesizes per-finger pressure tactile images from RGB observations, reducing the need for tactile-equipped data collection. FELT uses a large frozen visual encoder and a lightweight query decoder to predict tactile signals in a single feed-forward pass. To respect the physical topology of dual-finger tactile sensors, FELT decodes the left and right tactile sensor panels through separate branches, capturing the asymmetric contact patterns during interactions such as wiping, insertion, and in-hand rotation. At inference time, FELT only requires RGB data, allowing us to augment existing vision-only data with tactile observations, either as generated tactile images or as latent tactile features. Experiments on four contact-rich manipulation tasks demonstrate that both generated tactile images and latent tactile features improve policy success over vision-only baselines, with latent feature requiring no real tactile sensor during policy training or deployment. Supplementary material is available on our anonymous website: https://felt-tactile.github.io/.