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Perceptive General Motion Tracking pipeline for humanoid robots that learns terrain adaptation from independently selected motion references and terrains, providing a unified policy for terrain-adaptive locomotion, diverse whole-body behaviors, and teleoperation in complex environments.
XR-2 shows that scaling demonstration data and incorporating real-time corrections can dramatically enhance bimanual manipulation success rates in household tasks.
Robots using the SWAP model achieved record-breaking parkour feats, including a 2.13 m jump and a 1.63 m climb, demonstrating the power of symmetry in robotic learning.
Gaze behaviors learned through reinforcement alone can lead to unprecedented humanoid locomotion capabilities, including a record 1.2m gap traversal.
Achieving up to 46% token compression without sacrificing accuracy, HMPO revolutionizes the efficiency of chain-of-thought reasoning in large language models.
Stop letting Kubernetes control plane placement be an afterthought: RL can intelligently optimize it for substantial performance gains in multi-region deployments.
