Communicating the goal of a task to another person is easy: we can use language, show them an image of the desired outcome, point them to a how-to video, or use some combination of all of these. On the other hand, specifying a task to a robot for reinforcement learning requires substantial effort. Most prior work that has applied deep reinforcement learning to real robots makes uses of specialized sensors to obtain rewards or studies tasks where the robot’s internal sensors can be used to measure reward. For example, using thermal cameras for tracking fluids, or purpose-built computer vision systems for tracking objects. Since such instrumentation needs to be done for any new task that we may wish to learn, it poses a significant bottleneck to widespread adoption of reinforcement learning for robotics, and precludes the use of these methods directly in open-world environments that lack this instrumentation.
Wearing a sensor-packed glove while handling a variety of objects, MIT researchers have compiled a massive dataset that enables an AI system to recognize objects through touch alone. The information could be leveraged to help robots identify and manipulate objects, and may aid in prosthetics design.
Imagine a robot trying to learn how to stack blocks and push objects using visual inputs from a camera feed. In order to minimize cost and safety concerns, we want our robot to learn these skills with minimal interaction time, but efficient learning from complex sensory inputs such as images is difficult. This work introduces SOLAR, a new model-based reinforcement learning (RL) method that can learn skills – including manipulation tasks on a real Sawyer robot arm – directly from visual inputs with under an hour of interaction. To our knowledge, SOLAR is the most efficient RL method for solving real world image-based robotics tasks.
With aims of bringing more human-like reasoning to autonomous vehicles, MIT researchers have created a system that uses only simple maps and visual data to enable driverless cars to navigate routes in new, complex environments.
Returning from vacation, my inbox overflowed with emails announcing robot “firsts.” At the same time, my relaxed post-vacation disposition was quickly rocked by the news of the day and recent discussions regarding the extent of AI bias within New York’s financial system. These unrelated incidents are very much connected in representing the paradox of the acceleration of today’s inventions.
Humans have the ability to seamlessly adapt to changes in their environments: adults can learn to walk on crutches in just a few seconds, people can adapt almost instantaneously to picking up an object that is unexpectedly heavy, and children who can walk on flat ground can quickly adapt their gait to walk uphill without having to relearn how to walk. This adaptation is critical for functioning in the real world.
By Benjamin Boettner
Along developed riverbanks, physical barriers can help contain flooding and combat erosion. In arid regions, check dams can help retain soil after rainfall and restore damaged landscapes. In construction projects, metal plates can provide support for excavations, retaining walls on slopes, or permanent foundations. All of these applications can be addressed with the use of sheet piles, elements folded from flat material and driven vertically into the ground to form walls and stabilize soil.
MIT engineers have designed tiny robots that can help drug-delivery nanoparticles push their way out of the bloodstream and into a tumor or another disease site. Like crafts in “Fantastic Voyage” — a 1960s science fiction film in which a submarine crew shrinks in size and roams a body to repair damaged cells — the robots swim through the bloodstream, creating a current that drags nanoparticles along with them.
European Robotics Forum, the most influential meeting of the robotics and AI community, held its 10th anniversary edition in Romania. The event was organized Under the High Patronage of the President of Romania and Under the Patronage of the Romanian Presidency of the Council of the European Union.
A new learning system developed by MIT researchers improves robots’ abilities to mold materials into target shapes and make predictions about interacting with solid objects and liquids. The system, known as a learning-based particle simulator, could give industrial robots a more refined touch — and it may have fun applications in personal robotics, such as modelling clay shapes or rolling sticky rice for sushi.
In many animals, tool-use skills emerge from a combination of observational learning and experimentation. For example, by watching one another, chimpanzees can learn how to use twigs to “fish” for insects. Similarly, capuchin monkeys demonstrate the ability to wield sticks as sweeping tools to pull food closer to themselves. While one might wonder whether these are just illustrations of “monkey see, monkey do,” we believe these tool-use abilities indicate a greater level of intelligence.