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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

34:25

Geometry Is the Contact Plan: Bringing Surface...

Embodied AI 101 ·

2026/09/04

Geometry-Informed Contact Optimization and Model Predictive Control for Efficient Contact-Rich Manipulation

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30:12

A Swarm That Knows Its Margin: Safe Multi-Drone...

Embodied AI 101 ·

2026/09/04

Safe and Scalable Multi-Drone Payload Transport via CBF-Based Reinforcement Learning With Zero-Shot Sim-to-Real Transfer

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30:54

The Optimizer Becomes a Policy: Inside RP-1's Learned...

Embodied AI 101 ·

2026/09/03

Pantheon Industries introduces Reinforced Planning (RP-1), the first fully learned planner that iteratively improves robot action plans from scratch using Reinforcement Learning over a frozen World Model. RP-1 reimagines planning as a learned policy: starting with a candidate action sequence, it uses a frozen World Model to imagine outcomes, a learned critic to evaluate closeness to goal (value-based scoring instead of latent distance), and a neural planner to revise the sequence over

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32:47

SAGA: Ground the Affordance, Then Learn the Motion

Embodied AI 101 ·

2026/09/03

SAGA: Open-World Mobile Manipulation via Structured Affordance Grounding

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31:55

REMAC: Turning Robot Failures into Better Team Plans

Embodied AI 101 ·

2026/09/03

REMAC: Self-reflective and self-evolving multi-agent collaboration for long-horizon robot manipulation

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31:29

Restore the Sensor Contract: Training-Free Robustness...

Embodied AI 101 ·

2026/09/02

Vision–Language–Action (VLA) policies remain brittle under modest distribution shift. On LIBERO-Plus, contemporary models that solve clean tasks at high rates can fall below 30% success when the camera's viewpoint or background changes. This paper proposes a training-free method to improve out-of-distribution (OOD) robustness for VLA policies.

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31:25

Before the Robot Touches Anything: Physics-Grounded...

Embodied AI 101 ·

2026/09/02

Embodied Tree of Thoughts: Deliberate Manipulation Planning With Embodied World Model

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34:24

MemoAct: Why Robot Memory Needs Both a Scratchpad and...

Embodied AI 101 ·

2026/09/01

MemoAct: Atkinson–Shiffrin-Inspired Hierarchical Memory-Augmented Policy for Robotic Manipulation

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33:40

When Touch Means Something: OmniVTLA and the Missing...

Embodied AI 101 ·

2026/09/01

OmniVTLA: Vision-Tactile-Language-Action Models With Semantic-Aligned Tactile Sensing

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33:06

Gaussians as State, Futures as Supervision: Inside...

Embodied AI 101 ·

2026/08/31

Introduction Learning robust visuomotor policies for bimanual manipulation remains challenging due to the stringent requirements for precise coordination between arms and the ability to generalize across diverse environmental conditions. Existing approaches struggle with generalization across diverse environmental conditions for long-horizon bimanual manipulation tasks.

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30:30

SIRModel and the Missing Geometry Layer in...

Embodied AI 101 ·

2026/08/31

Long-horizon robotic manipulation requires a policy to bridge task-level semantic reasoning with metric three-dimensional interaction geometry. Existing vision–language–action policies usually acquire geometry implicitly from visual tokens or introduce deterministic intermediate representations that lack spatial grounding. This paper proposes SIRModel, which learns a spatial intermediate representation to parameter-efficiently fine-tune a vision language model for robotic manipulation

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33:40

The Robot That Knows When to Think Twice: Inside τ0-VLA

Embodied AI 101 ·

2026/08/30

Long-horizon robot manipulation requires a robot to both execute individual skills reliably and sequence them coherently over extended tasks. Most hierarchical vision-language-action (VLA) models make each such decision with a fixed compute budget. τ0-VLA introduces world-model-guided test-time computation to a hierarchical VLA, enabling more deliberate planning for long-horizon tasks.

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29:53

When Should a Robot Stop Imagining? RISE and Adaptive...

Embodied AI 101 ·

2026/08/30

Introduces dynamic Roll/Stop decision-making in world action models that adapts imagination based on planning benefit, risk, and compute cost for more efficient robot planning. Enables world action models to selectively engage imagination only when beneficial.

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29:04

The Crib as a Causal Laboratory: How a Playing iCub...

Embodied AI 101 ·

2026/08/29

Learning sensorimotor contingencies—that is, the link between one's actions and their sensory effects—is fundamental to developing body knowledge, understanding causality, and developing a sense of agency. In developmental psychology, this...

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31:55

Feeling Around Corners: STRP Compresses Fingertip...

Embodied AI 101 ·

2026/08/29

High-voltage grid maintenance and chemical processing demand dexterous manipulation in confined, visually occluded environments. Existing teleoperation systems present a severe cost–fidelity trade-off: industrial platforms exceed USD 60,000 yet lack spatial force feedback. STRP addresses this with a low-cost platform featuring spatial force feedback.

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29:32

Give the Swarm a Pheromone Compass

Embodied AI 101 ·

2026/08/28

Search-and-rescue (SAR) operations in disaster environments require drone swarms to coordinate efficiently despite incomplete information and potential communication failures. Existing stigmergy-based approaches provide low-bandwidth coordination but rely on fixed rules, while multi-agent reinforcement learning (MARL) offers adaptive behavior but struggles with scalability and coordination. This paper proposes a novel approach combining directional pheromone gradient observations with

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36:12

When Every Weld Looks the Same: Language-Labeled...

Embodied AI 101 ·

2026/08/28

Abstract As industries increasingly move towards automation, the ability to visually localize objects and accurately estimate their pose, including rigid and non-rigid transformations, becomes critical. Conventional pose estimation methods are...

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26:58

When Robot Eyes Converge: Active Stereo for Humanoid...

Embodied AI 101 ·

2026/08/27

The design of robotic binocular camera systems has been inspired by human vision, as has their use in humanoid robots. One aspect of this inspiration has yet to play a role in robotics: convergent binocular stereo, where the cameras are angled inward to overlap their fields of view for improved depth perception.

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33:04

Two Clocks for Touch: How T-Rex Gives Dexterous VLAs...

Embodied AI 101 ·

2026/08/27

Introduces a full-stack co-designed tactile-reactive policy with asynchronous mixture-of-transformers (slow visuomotor planning + fast 4× tactile corrections), trained on the largest open 50-hour / 5,500-episode synchronized robot tactile play dataset collected on 22-DoF hands; bridges egocentric video pretraining to contact-rich manipulation via EgoScale mid-training.

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31:49

Let the VLM Decide, Let the Planner Drive: Inside Senna

Embodied AI 101 ·

2026/08/26

Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving

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