To handle diverse real-world tasks, robots must securely grasp objects of various shapes, textures, and sizes without unintentionally dropping them. Traditional methods improve this by increasing the robotic hand’s grip strength to avoid slippage.
Researchers Develop Bio-Inspired Motion Control to Prevent Slippage in Robotic Hands
Researchers from several universities and labs have proposed new methods to stop objects from slipping from robotic hands. Their technique adjusts the movement paths the hand follows during manipulation, rather than relying solely on grip force. The system, combining a robotic controller with bio-inspired trajectory modulation, was detailed in Nature Machine Intelligence.
“The idea for this work was inspired by a familiar human experience,” said Amir Ghalamzan, senior author of the study, in an interview with Tech Xplore.
Teaching Robots to Adjust Movements Like Humans to Protect Fragile Objects
When sensing a delicate object might slip, people adjust movements—slowing, tilting, or shifting—rather than just tightening their grip. In contrast, robots have traditionally relied on increasing grip strength, which can be ineffective and may even harm fragile items. Our goal was to explore ways to make robots respond more like humans in such situations,” explained Ghalamzan.
The study aimed to create a controller that predicts slip and adjusts movements, using bio-inspired trajectory modulation with grip-force control for more dexterous manipulation.
“Our method replicates the way humans rely on internal models to interact with their surroundings,” Ghalamzan said. Like the brain anticipating actions, the robot’s data-driven ‘world model’ predicts tactile feedback to detect and prevent slips in advance.
The controller lets robots adjust speed, direction, and hand position in real time instead of just increasing grip strength.. By securing objects through movement adjustments, this method can lower the risk of damaging delicate items. It also works when grip force can’t be changed, enabling more fluid, intelligent interactions.
Novel Motion-Based Slip Controller Enhances Grip-Force Control
“Our research delivers two major innovations,” Ghalamzan explained. First, we present a unique motion-based slip controller that complements grip-force control, useful when increasing grip isn’t possible.
“Second, we developed a predictive controller driven by a learned tactile forward model, or ‘world model,’ that enables robots to anticipate slip based on their intended actions.”
The team applied the new controller to plan a robotic gripper’s movements and tested it in dynamic, unstructured settings. In several cases, it notably enhanced grasp stability, surpassing conventional controllers that rely solely on adjusting grip force.
Ghalamzan noted that researchers have traditionally found embedding such a model within a predictive control loop too computationally intensive. “Our findings demonstrate that it is not only possible but also highly effective.”
World Model Could Broaden Robots’ Real-World Capabilities
This work could advance robotics by enabling safe physical and social interactions via a world model. Such capabilities could allow robots to handle diverse objects in real-world environments, from homes and manufacturing floors to healthcare facilities.
“We are working to make our predictive controller faster and more efficient for use in more demanding real-time scenarios,” Ghalamzan added. “This involves exploring new architectures and algorithms to minimize computational load.”
Future research will extend the system to handle more complex manipulation tasks, such as working with deformable items or objects requiring two-handed coordination. The team also plans to integrate computer vision, enabling trajectory planning that combines tactile and visual feedback.
“Another key goal is to improve the transparency and verifiability of these learned models,” Ghalamzan said. “As robots become more intelligent and autonomous, it’s essential that humans can understand and trust their decision-making. Our goal is to develop predictive controllers that are powerful, safe, and explainable for real-world use.
Read the original article on: Techxplore
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