Edward Johns, director of Imperial College London’s Robot Learning Lab, discusses robot manipulation, imitation learning, and AI with Robot Talk host Claire Asher.
Johns says the challenge is the huge range of tasks people expect robots to handle, from fitting a light bulb to tidying a room. Robots can do many tasks with simple grippers, so he sees AI and control as the current bottleneck more than hardware.
Johns describes teaching a robot by physically moving its own arm through a task while it records actions and camera observations. The resulting data trains a policy that selects actions based on what the robot sees. He sees this as a way to gather robotics data at scale and let non-specialists teach new tasks.
Johns explains DARBot, which uses image generation models to suggest how objects should be arranged for tasks such as setting a dinner table. The generated image can guide a robot without manually coding every placement rule. The team manually filtered images that had the wrong number of objects or unrealistic, decorative arrangements.
Johns points to warehouse sorting as a current application and adaptable manufacturing as a near-term possibility. Demonstrations could help a robot switch assembly tasks without rebuilding a production line. Home robots that clean and tidy remain harder because homes contain varied objects, safety concerns, and personal preferences.
Johns says Imperial College’s Robotics Forum connects researchers across departments because robotics spans fields such as computing, mechanical engineering, and bioengineering. The forum includes 40 principal investigators and more than 200 PhD students, and supports collaboration and industry engagement.
Briefs summarise talks and interviews; timestamps open the video at that moment. Claims in a talk are the speaker's own.
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