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PODCAST

Embodied AI 101

Embodied AI 101 tracks the latest research at the intersection of AI and physical intelligence. Robotics, manipulation, world models, and the path from digital intelligence to embodied agents — each episode breaks down recent papers and advances in the field. We examine the methodology, real-world implications, and path to deployment. Covering robot learning, perception, control, and the fundamental challenge of grounding intelligence in the physical world. New episodes daily.

technology

All Episodes

29:43

Four Keypoints, One Careful Pour: Connecting Language...

Embodied AI 101 ·

2026/10/06

Bridging VLM and KMP: Enabling Fine-Grained Robotic Manipulation via Semantic Keypoints Representation

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31:10

PEAfowl: Helping Two Arms Agree on What They See

Embodied AI 101 ·

2026/10/06

PEAfowl: Perception-Enhanced Multi-View Vision-Language-Action for Bimanual Manipulation

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28:35

MotorMind: Giving a Frozen VLM the Controls

Embodied AI 101 ·

2026/10/05

MotorMind leverages a frozen VLM with mid-level prospective actions and asynchronous feedback to control robots zero-shot on manipulation tasks, requiring no task-specific training. This advances robot foundation models by demonstrating strong generalization across manipulation benchmarks without fine-tuning.

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28:53

MAPF-World: Why a Little Foresight Can Beat a Bigger...

Embodied AI 101 ·

2026/10/05

MAPF-World: Action World Model for Multi-Agent Path Finding

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29:43

Digit Leaves the Lab: The Real Lesson of 500...

Embodied AI 101 ·

2026/10/04

An end-to-end policy trained on ~500 teleoperation demonstrations combined with a sim-based whole-body controller achieves 80–90% success on unseen, chaotic real-world environments without any zero-shot fine-tuning. Demonstrates strong generalization of learned manipulation policies on a full humanoid platform.

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28:53

VLS: Steering Frozen Robot Policies Without Retraining

Embodied AI 101 ·

2026/10/04

VLS steers frozen diffusion and flow-matching robot policies using vision-language models to handle out-of-distribution changes such as novel obstacles and lighting conditions, requiring no additional training data. This advances the adaptability of robot foundation models at inference time without retraining.

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30:08

CAST: Making Language Matter to Robot Policies

Embodied AI 101 ·

2026/10/03

CAST: Counterfactual Labels Improve Instruction Following in Vision-Language-Action Models

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28:39

When the Command Disappears: What Counts as Robot...

Embodied AI 101 ·

2026/10/03

Robot Independent Intelligence: When Language Vanishes in Vision-Language-Action Models

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30:31

Fast Plans Are Not Enough: Does the Robot Actually...

Embodied AI 101 ·

2026/10/02

Introduces a hierarchical VLA architecture that better bridges high-level language planning and low-level robot execution, reducing the gap between what a model plans and what it physically does. This advances robot foundation models by improving real-world action fidelity from vision-language reasoning.

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10:51

Atlas's New Hands: Built to Learn, Not Just to Look...

Embodied AI 101 ·

2026/10/02

Atlas is upgraded with 13 DoF dexterous hands featuring tactile sensing, direct drive actuation, and sim-to-real RL training, designed for real tool use and in-hand manipulation at scale. Represents a significant hardware-software milestone for humanoid dexterous manipulation in production.

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28:27

Astra Gets a Body: What Zero-Shot Humanoid Control...

Embodied AI 101 ·

2026/10/01

Astra demonstrates zero-shot open-ended pick-and-place on a physical humanoid robot, bridging simulation tooling directly to real-world execution without task-specific fine-tuning. This represents a meaningful step in sim-to-real generalization for humanoid manipulation.

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29:24

Physical Coding: Robots That Check Their Work—and...

Embodied AI 101 ·

2026/09/30

A new paper introduces self-evolving coding agents for robotics that represent task state and execution as code, enabling robots to verify, recover from failures, and evolve policies through real-world experience via the HexaAnything harness. This advances sim-to-real transfer by grounding robot learning in executable, verifiable code representations.

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26:57

SpatialClaw: Give the VLM a Geometry Notebook

Embodied AI 101 ·

2026/09/30

SpatialClaw is an NVIDIA spatial reasoning agent that uses Python-based code generation to perform 3D spatial reasoning, achieving +13.6 average improvement across 20 benchmarks with open vision-language models. The approach advances how embodied agents reason about 3D structure and spatial relationships without task-specific fine-tuning.

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29:07

Real-Time EXPO-FT: Slow VLA, Fast Corrections, Better...

Embodied AI 101 ·

2026/09/29

Async RL fine-tuning via EXPO-FT enables π0.5 to handle highly dynamic manipulation tasks such as ball balancing and striking in real time. The approach demonstrates that post-training with RL can unlock dynamic capabilities not achievable through imitation learning alone.

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28:18

Aether: From Plausible Futures to Feasible Robot Plans

Embodied AI 101 ·

2026/09/29

Aether's unified world model jointly learns environment dynamics and long-term physical state evolution under physical constraints, enabling robots to plan feasible trajectories over extended horizons. The demo highlights how constraint-aware world modeling can prevent physically implausible plans that plague standard generative approaches.

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27:22

ARLI: Fixing the Missing State in Asynchronous Robot RL

Embodied AI 101 ·

2026/09/28

Proposes a framework that restores the Markov property for RL fine-tuning of VLAs despite asynchronous inference delays, enabling effective RL training on large vision-language-action models. This directly unblocks post-training RL for frontier robot policies operating under real-world latency constraints.

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29:15

Flex-π: Teach Geometry, Choose How Much to Imagine

Embodied AI 101 ·

2026/09/28

A 6B-parameter world-action model that jointly predicts 3D pointmaps and DINO features alongside RGB frames, improving demonstration efficiency, generalization, and long-horizon task performance in robotics. Integrating structured 3D world predictions into the action loop represents a meaningful advance in robot foundation model design.

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28:36

SkipVLA: Let the Planner Move, Let the VLA Manipulate

Embodied AI 101 ·

2026/09/27

SkipVLA proposes a hybrid architecture combining a classical planner with a Vision-Language-Action model for manipulation tasks, achieving 2.5x faster execution and 59.6% lower latency on a physical robot arm. The approach demonstrates that structured planning can significantly reduce the computational burden of end-to-end VLA inference.

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28:34

ForeDrive: A World Model Is Only Useful If It Changes...

Embodied AI 101 ·

2026/09/27

ForeDrive introduces a planning-relevant latent world model that guides autonomous driving through foresight, enabling the agent to anticipate future states before acting. The approach advances world-model-based driving by tightly coupling latent prediction with planning objectives.

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29:59

DO AS I DO: Making Human Video Executable

Embodied AI 101 ·

2026/09/26

A new algorithm reconstructs and retargets monocular RGB (or synthetically generated) videos to high-DoF robot hands, enabling scalable dexterous manipulation data collection that outperforms current state-of-the-art retargeting methods. This matters because it removes the need for expensive motion capture or teleoperation hardware to generate training data for dexterous robots.

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379 results

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