Embodied AI 101

Shaoqing Tan
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Stay in the loop on research in AI and physical intelligence.

  1. 20h ago

    One Policy, Three Bodies, Zero Messages: Inside CHORUS

    Trains a single VLA policy that enables multiple heterogeneous robots to collaborate on complex manipulation tasks using only local observations and robot IDs, avoiding centralized controllers.

  2. 20h ago

    Anchor the Semantics, Align the Actions: A Better Way to Fine-Tune VLAs

    Proposes a VLA fine-tuning technique that preserves pretrained VLM representations while aligning language to actions, raising real-robot generalization success from 28% to 54%.

  3. 1d ago

    Learning the Rule, Not the Gait: Stick-Insect Behavior as a Portable Robot Objective

    Insect locomotion exhibits remarkable adaptability and flexibility despite the limited scale of its nervous system. However, the underlying principles that govern leg coordination remain difficult to extract and model. This paper presents an adversarial inverse reinforcement learning approach to infer transferable locomotor principles from limited insect behavioral data, enabling robust robot locomotion.

  4. 1d ago

    Two Brains Under Pressure: How UnderwaterVLA Connects Language Models to AUV Control

    The UnderwaterVLA dual-brain vision-language-action architecture enables robust autonomous underwater navigation

  5. 1d ago

    From Demo to Duty: RL-100's Real-World Reinforcement Learning Flywheel

    Real-world robotic manipulation in homes and factories demands reliability, efficiency, and robustness that approach or surpass skilled human operators. We present a real-world reinforcement learning (RL) framework, RL-100, for achieving performant robotic manipulation through real-world RL training.

  6. 1d ago

    Distilling a Swarm into 158 Million Parameters: Inside MiniUAV-VLA

    Coordinating multiple unmanned aerial vehicles (UAVs) for cooperative missions requires agents that perceive their environment, reason about objectives, and generate joint actions. Vision–language–action (VLA) models unify these capabilities but lack efficiency for real-world multi-UAV deployment. This episode explores MiniUAV-VLA, a compact 158M-parameter model that uses MARL expert distillation to enable cooperative multi-UAV search and elimination missions.

  7. Jul 23

    Imagining Locomotion: Learning a Neural World Model for Legged Robots

    A neural dynamics world model paired with model-free RL policies for quadruped and humanoid locomotion in IsaacLab, enabling long-horizon autoregressive prediction and imagined rollouts that outperform pure model-based RL in prediction accuracy, policy learning, and sim-to-real transfer.

  8. Jul 23

    One Model to See, Plan, and Act: Introducing EO-1 for Embodied AI

    A 3B parameter unified decoder-only transformer that interleaves vision, text, and action tokens for perception, planning, reasoning, and control in a single model. Trained on the 1.5M-sample EO-1.5M dataset with strong results across manipulation tasks and benchmarks.

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About

Stay in the loop on research in AI and physical intelligence.

Information

  • Creator
    Shaoqing Tan
  • Years Active
    2025 - 2026
  • Episodes
    236
  • Rating
    Clean
  • Copyright
    © 2026 Shaoqing Tan