AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Handroid is introduced, a desktop-scale dual-embodiment robot that integrates both capabilities within a single reconfigurable platform and provides a unified control and learning framework supporting hand teleoperation, dexterous grasping, in-hand manipulation, humanoid locomotion, gait generation, and interactive mot...
Building general-purpose dexterous manipulation policies requires benchmarks that go beyond isolated tasks to systematically evaluate policies across diverse interaction modes, sensory conditions, and robot embodiments. However, existing benchmarks remain limited in task and data diversity, embodiment coverage, or cont...
Experiments show that DenseReward outperforms general-purpose VLMs and existing robotic reward models in dense reward prediction across both simulated and real-world manipulation, and provides effective reward guidance for downstream model predictive control and reinforcement learning.
The effectiveness of Any body, a unified whole-body humanoid controller driven by an arbitrary subset of body keypoints chosen at deploy time, is demonstrated by tracking large-scale human motions from arbitrary keypoint subsets, free-form control, flexibly teleoperating, and learning downstream behaviors including loc...
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