AI News, Robots visualize actions, plan, with out human instruction
Robots visualize actions, plan, with out human instruction
Sergey Levine and UC Berkeley colleagues have developed robotic learning technology that enables robots to visualize how different behaviors will affect the world around them, with out human instruction.
Speakers include: Vinod Khosla – Justin Sanchez – Brian Otis – Bryan Johnson – Zhenan Bao – Nathan Intrator – Carla Pugh – Jamshid Ghajar – Mark Kendall – Robert Greenberg – Darin Okuda – Jason Heikenfeld – Bob Knight – Phillip Alvelda – Paul Nuyujukian – Peter Fischer – Tony Chahine – Shahin Farshchi – Ambar Bhattacharyya – Adam D’Augelli – Juan-Pablo Mas – Michael Eggleston – Walter Greenleaf
Reversal Learning Task in Children with Autism Spectrum Disorder: A Robot-Based Approach.
Children with autism spectrum disorder (ASD) engage in highly perseverative and inflexible behaviours.
The aim of our study is to investigate the role of the robotic toy Keepon in a cognitive flexibility task performed by children with ASD and typically developing (TD) children.
On the other hand their cognitive flexibility performance is, in general, similar in the robot and the human conditions with the exception of the learning phase where the robot can interfere with the performance.
Last month, we showed an earlier version of this robot where we'd trained its vision system using domain randomization, that is, by showing it simulated objects with a variety of color, backgrounds, and textures, without the use of any real images.
(The vision system is never trained on a real image.) The imitation network observes a demonstration, processes it to infer the intent of the task, and then accomplishes the intent starting from another starting configuration.
Applied to block stacking, the training data consists of pairs of trajectories that stack blocks into a matching set of towers in the same order, but start from different start states.
At test time, the imitation network was able to parse demonstrations produced by a human, even though it had never seen messy human data before.
The imitation network uses soft attention over the demonstration trajectory and the state vector which represents the locations of the blocks, allowing the system to work with demonstrations of variable length.
It also performs attention over the locations of the different blocks, allowing it to imitate longer trajectories than it's ever seen, and stack blocks into a configuration that has more blocks than any demonstration in its training data.
- On Wednesday, September 18, 2019
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