From the 1st-5th June, the robots descended on Vienna. The 2026 IEEE International Conference on Robotics & Automation (ICRA) brought together the top minds in robotics for one short week to showcase the latest technologies, form new collaborations, and exchange ideas. Held at the Messe Wien, a stone's throw from the bank of the Danube, ICRA proved to be equal parts technological marvel and thought-provoking discussion. The host venue for ICRA 2026: Messe Wien, also known as VIECON. My week at ICRA began with the 2nd ICRA 2026 Workshop on Robot Ethics: Ethical, Legal and User Perspectives in Robotics & Automation (WOROBET) .

Comparison of RAPID, LEAP, and Allegro infinger thickness, MCP joint configurations, and dexterity, illustrated with representative finger poses.

Garment manipulation is a critical challenge due to the diversity in garment categories, geometries, and deformations. Despite this, humans can effortlessly handle garments, thanks to the dexterity of our hands. However, existing research in the field has struggled to replicate this level of dexterity, primarily hindered by the lack of realistic simulations of dexterous garment manipulation. Therefore, we propose DexGarmentLab, the first environment specifically designed for dexterous (especially bimanual) garment manipulation, which features large-scale high-quality 3D assets for 15 task scenarios, and refines simulation techniques tailored for garment modeling to reduce the sim-to-real gap. Previous data collection typically relies on teleoperation or training expert reinforcement learning (RL) policies, which are labor-intensive and inefficient. In this paper, we leverage garment structural correspondence to automatically generate a dataset with diverse trajectories using only a single expert demonstration, significantly reducing manual intervention.

Human hands possess remarkable dexterity and have long served as a source of inspiration for robotic manipulation. In this work, we propose a human HandInformed visual representation learning framework to solve difficult Dexterous manipulation tasks (H-InDex) with reinforcement learning. Our framework consists of three stages: (i) pre-training representations with 3D human hand pose estimation, (ii) offline adapting representations with self-supervised keypoint detection, and (iii) reinforcement learning with exponential moving average BatchNorm. The last two stages only modify 0.36%parameters of the pre-trained representation in total, ensuring the knowledge from pre-training is maintained to the full extent. We empirically study 12 challenging dexterous manipulation tasks and find that HInDex largely surpasses strong baseline methods and the recent visual foundation models for motor control. Code is available at yanjieze.com/H-InDex.

From sorting chicken nuggets to screwing in light bulbs, Eka's robots are eerily lifelike. But do they have real physical smarts? It starts gingerly pawing around the table, as if searching for its glasses on the nightstand. It gently positions the bulb between its two pincers. The claw goes chasing it across the table. After a few nips, the bulb is back in its grasp. In more than a decade of writing about robots, I have never seen one move so naturally.

Biological synergies have emerged as a widely adopted paradigm for dexterous hand design, enabling human-like manipulation with a small number of actuators. Nonetheless, excessive coupling tends to diminish the dexterity of hands. This paper tackles the trade-off between actuation complexity and dexterity by proposing an anthropomorphic finger topology with 4 DoFs driven by 2 actuators, and by developing an adaptive, modular dexterous hand based on this finger topology. We explore the biological basis of hand synergies and human gesture analysis, translating joint-level coordination and structural attributes into a modular finger architecture. Leveraging these biomimetic mappings, we design a five-finger modular hand and establish its kinematic model to analyze adaptive grasping and in-hand manipulation. Finally, we construct a physical prototype and conduct preliminary experiments, which validate the effectiveness of the proposed design and analysis.

This paper proposes Tac2Motion, a contact-aware reinforcement learning framework to facilitate the learning of contact-rich in-hand manipulation tasks, such as removing a lid. To this end, we propose tactile sensing-based reward shaping and incorporate the sensing into the observation space through embedding. The designed rewards encourage an agent to ensure firm grasping and smooth finger gaiting at the same time, leading to higher data efficiency and robust performance compared to the baseline. We verify the proposed framework on the opening a lid scenario, showing generalization of the trained policy into a couple of object types and various dynamics such as torsional friction. Lastly, the learned policy is demonstrated on the multi-fingered robot, Shadow Robot, showing that the control policy can be transferred to the real world. The video is available: https://youtu.be/poeJBPR7urQ.