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Robotic Table Tennis: A Case Study into a High Speed Learning System
We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets.
Bi-Manual Block Assembly via Sim-to-Real Reinforcement Learning
Without manually-designed controller nor human demonstrations, we demonstrate that with careful Sim2Real considerations, our policies trained with RL in simulation enable two xArm6 robots to solve the U-shape assembly task with a success rate of above90% in simulation, and 50% on real hardware without any additional real-world fine-tuning.
Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learning
Most successes in robotic manipulation have been restricted to single-arm robots, which limits the range of solvable tasks to pick-and-place, insertion, and objects rearrangement.
Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning
Despite the simplicity of this objective, the compositional nature of building diverse blueprints from a set of blocks leads to an explosion of complexity in structures that agents encounter.
