With its opposable thumb, multiple joints and gripping skin, human hands are often considered to be the pinnacle of dexterity, and many robotic hands are designed in their image. But having been shaped by the slow process of evolution, human hands are far from optimized, with the biggest drawbacks including our single, asymmetrical thumbs and attachment to arms with limited mobility. "We can easily see the limitations of the human hand when attempting to reach objects underneath furniture or behind shelves, or performing simultaneous tasks like holding a bottle while picking up a chip can," says Aude Billard, head of the Learning Algorithms and Systems Laboratory (LASA) in EPFL's School of Engineering. "Likewise, accessing objects positioned behind the hand while keeping the grip stable can be extremely challenging, requiring awkward wrist contortions or body repositioning." A team composed of Billard, LASA researcher Xiao Gao, and Kai Junge and Josie Hughes from the Computational Robot Design and Fabrication Lab designed a robotic hand that overcomes these challenges.

W e are also committed to releasing the code. Implementation details for Stage 2. Our implementation strictly follows the previous work that also In this section, we briefly introduce our tasks. It requires the robot hand to open the door on the table. It requires the robot hand to orient the pen to the target orientation. It requires the robot hand to place the object on the table into the mug. We present the success rates of our six task categories as in Table 1.

This paper explores a novel task "Dexterous Grasp as Y ou Say" (DexGYS), enabling robots to perform dexterous grasping based on human commands expressed

Our framework enables the development of the first fully data-driven solutions to active touch on top of learned models for object understanding.

ActionSense: A Multimodal Dataset and Recording Framework for Human Activities Using Wearable Sensors in a Kitchen Environment

In the horticultural world, some vines are especially grabby. As they grow, the woody tendrils can wrap around obstacles with enough force to pull down entire fences and trees. Inspired by vines' twisty tenacity, engineers at MIT and Stanford University have developed a robotic gripper that can snake around and lift a variety of objects, including a glass vase and a watermelon, offering a gentler approach compared to conventional gripper designs. A larger version of the robo-tendrils can also safely lift a human out of bed. The new bot consists of a pressurized box, positioned near the target object, from which long, vine-like tubes inflate and grow, like socks being turned inside out.

Breakthroughs, discoveries, and DIY tips sent six days a week. Engineers in Switzerland recently created a detachable, spider-like robot hand capable of grabbing multiple objects and using its fingers to crawl. The unsettling device, reminiscent of a threatening video game creature, can separate itself from a mounted robot arm, tip-toe (or really, tip-) its way toward small objects, pick them up, and carry them on its back. The symmetrical design and flexible fingers mean that the robot can transport objects on either side of its body. For humans, that would look like holding a ball in your palm while simultaneously grasping a piece of fruit on the back of your hand.

Sharpa's ping-pong playing, blackjack dealing humanoid is working overtime at CES 2026 The company's super dexterous robotic hand, SharpaWave, allows it to pull individual playing cards from a deck. There were no idle hands at Sharpa's CES booth. The company's humanoid may have been the busiest bot at show, autonomously playing ping-pong, dealing blackjack games and taking selfies with passersby. The hand has 22 active degrees of freedom, according to the company, allowing for precise and intricate finger movements. It mirrored my gestures as I wiggled my hand in front of its camera, getting everything mostly right, which was honestly pretty cool.

Over the course of the year, we've had the pleasure of working with many talented researchers from across the globe. As 2025 draws to a close, we take a look back at some of the excellent blog posts, interviews and podcasts from our contributors. Jiahui Zhang and Jesse Zhang to tell us about their framework for learning robot manipulation tasks solely from language instructions without per-task demonstrations. Hui Zhang writes about work presented at CoRL2025 on RobustDexGrasp, a novel framework that tackles different grasping challenges with targeted solutions. In this podcast from AAAI, host Ella Lan asked Professor Marynel Vázquez about what inspired her research direction, how her perspective on human-robot interactions has changed over time, robots navigating the social world, and more.