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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

31:15

Seeing the Nearly Invisible: Stereo Vision for...

Embodied AI 101 ·

2026/08/26

This study develops a binocular stereo vision system to enhance the operational safety of agricultural unmanned aerial vehicles (UAVs) by detecting and localizing thin cables—one of the most prevalent hazards in low-altitude flight.

en

32:00

Taming the Flying Tip: Constrained MPC for Dynamic...

Embodied AI 101 ·

2026/08/25

Deformable linear objects (DLOs) exhibit highly nonlinear dynamic behavior, complicating their control during high-speed maneuvers. Furthermore, the lack of a generic spatial representation and the difficulty of real-time state estimation...

en

30:48

Replay Is Not Reality: Embodied RL When Both Policy...

Embodied AI 101 ·

2026/08/25

Abstract Embodied agents must continuously adapt to the physical world using interaction data collected across varying timescales, controllers, and environmental conditions. However, standard reinforcement learning assumes stationary dynamics and on-policy data collection, which breaks down when agents must learn from heterogeneous data sources with non-stationary dynamics and off-policy behavior.

en

34:22

PPO Beyond the Simulator: What a TurtleBot3 Mapless...

Embodied AI 101 ·

2026/08/24

Objective: In the field of mobile robotics, autonomous navigation in dynamic environments is one of the most challenging tasks in these environments: traditional methods based on pre-mapping and geometric planning...

en

34:02

VLAs in the Wild: What 142 Papers Really Say About...

Embodied AI 101 ·

2026/08/23

Generalist robots need to perform diverse tasks while operating in dynamic, uncertain, and unstructured environments, often around human beings. Vision-language-action (VLA) models have recently emerged as a promising and flexible approach to enabling generalist robot behavior. This is a systematic literature review of VLA models for generalist robots.

en

30:06

VEDA and the Moment Vision Stops Being Enough

Embodied AI 101 ·

2026/08/22

Industrial humanoid robots are increasingly expected to execute high-precision manipulation tasks, such as assembly, welding, and material handling, under dynamic and contact-rich working conditions.

en

32:41

Three Ways to Buy Back Robot Data: Augment the...

Embodied AI 101 ·

2026/08/21

Abstract Robots have the potential to assist people in homes, hospitals, warehouses, and factories, but today's systems still struggle to learn robust manipulation skills from limited data, especially in contact-rich tasks. This paper explores data-efficient robot learning approaches for contact-rich manipulation.

en

31:01

UI-Mate: Show the Desktop Agent Once, Then Let It Replan

Embodied AI 101 ·

2026/08/20

A 27B model that achieves one-shot doubling of strict success rates on long-horizon office automation tasks via demonstration-conditioned policy learning.

en

31:07

The Frozen VLM That Learns Anyway: Inside Spatial...

Embodied AI 101 ·

2026/08/20

A parameter-update-free framework that distills verified experience into transferable spatial memory for frozen VLMs, achieving consistent gains across 4 VLMs and 5 spatial reasoning benchmarks.

en

32:35

The LLM Proposes, the Planner Disposes: VLA-SP for...

Embodied AI 101 ·

2026/08/19

Abstract Cross-modal foundation models are increasingly used for robotic task understanding and planning. However, connecting multimodal observations and natural language instructions to symbolic planners and executable robot actions remains a challenge. This paper addresses long-horizon robotic manipulation tasks using LLM-driven symbolic planning.

en

30:59

When Vision Cannot Know: MulPlanLM Lets Robot...

Embodied AI 101 ·

2026/08/19

MulPlanLM: multimodal robotic task planning with vision-language models and physical feedback

en

27:51

Reading Trust in Motion: A Multimodal Perception...

Embodied AI 101 ·

2026/08/18

Abstract Industry 5.0 promotes collaborative manufacturing environments where humans and robots work together, leveraging their complementary strengths to improve productivity and safety. In such settings, trust is fundamental to ensuring effective collaboration between humans and robots. This paper presents a multi-modal model for trust evaluation in industrial human-robot collaboration settings.

en

31:42

Two MPCs, One Robot: Bézier Curves for Fast,...

Embodied AI 101 ·

2026/08/18

Dual-arm mobile manipulators can transport and manipulate large objects with simple end-effectors. Interacting with dynamic environments subject to strict safety and compliance requirements, achieving whole-body motion planning online while maintaining compliance is challenging. This paper presents an efficient whole-body model predictive control approach for online compliant dual-arm mobile manipulation.

en

29:18

The Reward Behind the Gait: Learning Transferable...

Embodied AI 101 ·

2026/08/17

Insect locomotion exhibits remarkable adaptability and flexibility despite the limited scale of its nervous system. This paper extracts and models underlying leg coordination principles via adversarial inverse reinforcement learning, enabling transferable robot locomotion from limited biological data.

en

30:28

Beyond Looking Human: Bio-Functional Robot Design for...

Embodied AI 101 ·

2026/08/17

Dexterous hands and robotic manipulators as physical interfaces for diverse environments. Proposes bio-functional mimicry beyond structural biomimicry, enabling cross-scale manipulation and limb-tool integration for versatile robotic tasks.

en

34:55

LDA-1B and the Case for Never Throwing Robot Data Away

Embodied AI 101 ·

2026/08/16

Scales a latent dynamics action model to 1B parameters by ingesting diverse embodied datasets into a unified training pipeline for generalist robot policies.

en

38:07

Flex-π: One Robot Policy, Three Futures, Fifty-Six...

Embodied AI 101 ·

2026/08/12

Jointly predicts future RGB frames, 3D pointmaps, DINO semantics, and actions within a unified world-action model. Can be deployed flexibly as a VLA, full world-action model, or hybrid from a single checkpoint.

en

34:35

Imagined Motion, Grounded Geometry: How Rest2Art...

Embodied AI 101 ·

2026/08/12

An ECCV 2026 paper that converts a single static closed-state image into a simulation-ready articulated asset using video diffusion for joint hypothesis generation, requiring no motion data. Enables articulated object reconstruction without dynamic observations.

en

32:14

ComBodied Agents: When the Human Becomes the World Model

Embodied AI 101 ·

2026/08/12

Proposes a new agent architecture that unifies digital and physical (embodied) action spaces, supporting human-state trajectories with explicit handling of consent, uncertainty, and reversibility for safer human-robot interaction.

en

33:59

A Sphere Every Robot Hand Can Speak

Embodied AI 101 ·

2026/08/11

Introduces a unified hand action space that enables seamless cross-embodiment transfer for dexterous manipulation policies across different robot hands.

en

381 results

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