Flying robotic systems have already proved to be highly promising for tackling numerous real-world problems, including explorations of remote environments, the delivery of packages in inaccessible sites, and searches for survivors of natural disasters. In recent years, roboticists and computer scientists have introduced a multitude of aerial vehicle designs, each with distinct advantages and features.
An innovative bimanual robot displays tactile sensitivity close to human-level dexterity using AI to inform its actions.
Ever wonder why the most advanced robots always seem to have hard bodies? Why not more pliable ones, like humans have?
The search for innovative materials will be greatly assisted by software that can suggest new experimental possibilities and also control the robotic systems that check them out.
A tailsitter is a fixed-wing aircraft that takes off and lands vertically (it sits on its tail on the landing pad), and then tilts horizontally for forward flight. Faster and more efficient than quadcopter drones, these versatile aircraft can fly over a large area like an airplane but also hover like a helicopter, making them well-suited for tasks like search-and-rescue or parcel delivery.
Most adult humans are innately able to pick up objects in their environment and hold them in ways that facilitate their use. For instance, when picking up a cooking utensil, they would normally grab it from the side that will not be placed inside the cooking pot or pan.
Georgia Tech Ph.D. student Niranjan Kumar has created the Cascaded Compositional Residual Learning (CCRL) framework, enabling a quadrupedal robot to perform increasingly complex tasks without relearning motions, mirroring human learning. Kumar showcased by the robot opening a heavy door using energy transfer, a remarkable achievement in robotics.
A study published in Engineering introduces a novel image-based visual servoing (IBVS) method for unmanned aerial vehicles (UAVs) to track dynamic targets in GPS-denied environments.
Adding legs to robots that have minimal awareness of the environment around them can help the robots operate more effectively in difficult terrain, my colleagues and I found.
We've watched the remarkable evolution of robotics over the past decade with models that can walk, talk and make gestures like humans, undertake tasks from moving heavy machinery to delicately manipulating tiny objects, and maintain balance on two or four legs over rough and hostile terrain.
RoboCup is an international annual event designed to showcase advances in the fields of robotics and artificial intelligence (AI). At this event, different teams of humanoid robots play soccer against each other,
Dartmouth researchers are crafting soft robotic blocks that can work in unison to create structures able to bear weight, roll, walk, grip objects and transport loads.
Humans often take their fine motor abilities for granted. Recreating the mechanical precision of the human body is no easy task—one that graduate students in CMU's Mechanical Engineering Department hope to simplify through artificial intelligence.
Modular robots—robotic systems that can adapt their body configuration to change locomotion style or move on different terrains—can be highly advantageous for tackling missions in diverse environments. Over the past decade or so, engineers have developed a wide range of modular robots that rely on different designs and underlying mechanisms.
Motion capture (mocap) systems, technologies that can detect and record the movements of humans, animals and objects, are widely used in various settings. For instance, they have been used to shoot movies, to create animations with realistic lip and body movements, in interactive videogame consoles, or even to control robots.