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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.

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All Episodes

30:30

GAM: Ground the Target Before You Learn the Action

Embodied AI 101 ·

2026/09/26

GAM is a robot foundation model that conditions action chunk prediction on 3D grounded inputs — including language, 3D point clouds, and bounding boxes — alongside robot history, achieving strong results on LIBERO benchmarks and real-robot tasks. The 3D grounding mechanism advances how embodied agents spatially understand and act in their environment.

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28:44

Tune Slowly, Control Quickly: Learning a Better Robot...

Embodied AI 101 ·

2026/09/25

Autonomous navigation in complex, unstructured environments poses a significant challenge, with traditional planners lacking adaptability and end-to-end learning methods hindered by data dependency or training instability. This paper proposes a hierarchical learning-based hybrid method with sim-to-real transfer.

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29:21

THAW-VLA: World-Model Features Without World-Model...

Embodied AI 101 ·

2026/09/24

This work distills frozen world-model representations into compact VLAs via a single feature-alignment loss at training time, with the teacher cached once and absent at inference; a 0.8B student achieves 97.9% on LIBERO and lifts RoboCasa-GR1 humanoid performance from 48.2% to 50.5% at zero extra runtime cost (32 ms / 1.86 GB on RTX 5090). The approach generalizes across scales and architectures on both single-arm and bimanual real hardware.

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28:36

Joga: Why a Soccer Humanoid Needs to Control Its Gaze

Embodied AI 101 ·

2026/09/24

Joga demonstrates agile humanoid soccer on a Unitree G1 using an actuated neck and residual models to close the sim-to-real gap for both perception and actuation, enabling dribbling, tight turns, and receive→dribble→shoot sequences with fully onboard perception. This is a significant step toward general-purpose humanoid locomotion and active spatial perception in unstructured environments.

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29:10

AthenaZero: Why Dynamic Manipulation Starts With...

Embodied AI 101 ·

2026/09/23

AthenaZero is a bimanual manipulator designed to minimize inertia without compromising control authority. By using quasidirect drive actuation and transmission remotization techniques, the system achieves an effective end-point mass comparable to state-of-the-art systems while enabling dynamic, contact-rich manipulation. A significant hardware advance for dexterous embodied manipulation research.

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29:42

FROA-Drive and the Hard Part of Targeted Policy Repair

Embodied AI 101 ·

2026/09/23

Vision–Language–Action (VLA) models have shown strong potential for end-to-end autonomous driving, yet their post-training commonly relies on expensive simulator interaction or global policy updates. FROA-Drive introduces a failure-routed offline adaptation method that enables lightweight, targeted policy improvement for already-competent pretrained VLA driving policies without costly retraining or simulator access.

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28:09

Teach the Robot Where to Act—Then Make It Fast

Embodied AI 101 ·

2026/09/22

We present a framework for assistive robot manipulation that addresses two fundamental challenges: efficient adaptation of large-scale models for scene affordance understanding and effective learning of robot actions by grounding...

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28:48

HEAR: The Sound Your Robot Missed Between Actions

Embodied AI 101 ·

2026/09/22

Humans and animals use sound as a crucial cue for interacting with the physical world, as acoustic events can reveal contact, completion, hidden contents, or process state. Embodied agents should leverage sound for manipulation tasks. This paper presents the HEAR framework for sound-centric robot manipulation.

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29:53

VS-Splat: Learning Where 3D Detail Belongs

Embodied AI 101 ·

2026/09/20

A new feed-forward Gaussian Splatting method that selectively processes voxels for efficient 3D scene reconstruction, targeting real-time perception stacks for embodied agents. Advances the efficiency frontier of 3D scene representation without per-scene optimization.

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13:15

Spatial Search Comes to Quest — Beyond Pretty...

Embodied AI 101 ·

2026/09/20

Adapts a training-free Spatial Search prototype to Meta Quest's spatial data pipeline, enabling natural-language search over Gaussian Splatting and mesh scenes in real time on-device. Demonstrates practical deployment of 3D spatial intelligence for consumer AR/VR hardware.

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30:48

Occamy-1.0: Teaching a 35B Agent to Finish the Job

Embodied AI 101 ·

2026/09/19

Introduces a 35B cost-efficient model positioned at the Pareto frontier for collaborative long-horizon agent tasks, achieving performance competitive with significantly larger systems. Advances the capability of embodied and agentic AI to handle extended multi-step cooperative workflows.

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12:37

Helix 2.5: The House Changed. The Policy Didn't.

Embodied AI 101 ·

2026/09/18

Figure's Helix 2.5 humanoid robot enters 30 unseen Bay Area rental homes with no additional training and performs useful whole-body tasks for 4+ hours, demonstrating strong zero-shot generalization across diverse real-world environments. This is a notable advance in robot foundation model generalization for home deployment.

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12:21

The Hard Part Was the Stack: PI's Robot Goes to Work

Embodied AI 101 ·

2026/09/16

Physical Intelligence's mobile π robot runs fully autonomous for hours in a real production environment stacking boxes at Dandelion Chocolate, exposing generalization and reliability gaps between lab demos and production utility. Represents a significant milestone in deploying robot foundation models in unstructured real-world settings.

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13:40

ZDTaichu5.0-9B: Better Spatial Reasoning, an...

Embodied AI 101 ·

2026/09/16

ZDTaichu5.0-9B is a 9B edge-deployable multimodal model built on a Qwen3.5 backbone with C-RADIOv4 vision encoder that leads open sub-10B VLMs on spatial-reasoning benchmarks including ViewSpatial and MMSI-Bench, while supporting embodied AI and tool-use agent tasks. Its strong spatial reasoning performance at edge scale makes it particularly relevant for on-device robot perception.

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28:28

Light-Loco-Parkour: Learning the Skill Is Only Half...

Embodied AI 101 ·

2026/09/15

Light-Loco-Parkour presents a multi-skill distillation approach for learning perceptive whole-body parkour and locomotion skills deployable on real robots, enabling versatile agile movement across diverse terrains. The method advances embodied locomotion by combining perception and whole-body control into a unified distilled policy.

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29:18

OM-1: Human Skills, Many Bodies

Embodied AI 101 ·

2026/09/15

OM-1 is a robot foundation model trained exclusively on human manipulation data (no teleoperation or robot demonstrations) that achieves near human-level dexterity and zero-shot generalization across tabletop arms, industrial arms, and humanoids, including multi-robot collaboration. Its ability to transfer from human video data alone to diverse robot morphologies marks a notable step toward scalable robot learning.

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12:03

Show-Harness Gives VLMs a Robot Keyboard

Embodied AI 101 ·

2026/09/14

Uses discrete semantic actions paired with embodiment-specific interpreters to turn any pretrained VLM into a robot controller without a dedicated policy network or additional pretraining. Demonstrates strong zero-shot generalization across diverse tasks and environments.

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29:54

BridgeVLA++: Keep the Geometry, Give the Robot a Memory

Embodied AI 101 ·

2026/09/14

Renders point clouds as three orthographic 2D views fed into a pretrained PaliGemma VLM, predicting heatmaps to recover 6-DoF actions with temporal/spatial memory modules. Achieves 95.4% success on 13 real Franka tasks with only 3 demos per task, outperforming π0, RVT-2, and 3D Diffuser Actor, with fully open weights and code.

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28:03

The Sim-to-Real Gap Inside the Gearbox

Embodied AI 101 ·

2026/09/13

Reinforcement learning (RL) has become a powerful tool for quadrupedal locomotion, and a sim-to-real approach is widely adopted to avoid hardware damage during training. However, the sim-to-real gap remains a significant challenge.

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28:42

A Stable Grasp Isn't Enough: Giving 6-DoF Grasping a...

Embodied AI 101 ·

2026/09/13

Robotic grasping in cluttered scenes is dominated by methods that optimise either stability or a predefined task category, leaving a gap between 'can this grasp hold the object' and 'can this grasp serve the task'. This paper bridges that gap using foundation model semantic embeddings.

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379 results

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