Shows / Embodied AI 101
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.
Episodes
- Embodied AI 101
GigaBrain-0.7: Scaling Robot Intelligence by Separating Acting, Thinking, and Imagining
Introduces a three-system architecture for scaling embodied foundation models, representing a new approach to large-scale embodied AI. In this episode of…
- Embodied AI 101
Geometry Is the Contact Plan: Bringing Surface Reasoning Into MPC
Geometry-Informed Contact Optimization and Model Predictive Control for Efficient Contact-Rich Manipulation In this episode of Embodied AI 101, we explore…
- Embodied AI 101
A Swarm That Knows Its Margin: Safe Multi-Drone Transport With CBF-RL
Safe and Scalable Multi-Drone Payload Transport via CBF-Based Reinforcement Learning With Zero-Shot Sim-to-Real Transfer In this episode of Embodied AI 101, we…
- Embodied AI 101
The Optimizer Becomes a Policy: Inside RP-1's Learned Planner for Robot World Models
Pantheon Industries introduces Reinforced Planning (RP-1), the first fully learned planner that iteratively improves robot action plans from scratch using…
- Embodied AI 101
SAGA: Ground the Affordance, Then Learn the Motion
SAGA: Open-World Mobile Manipulation via Structured Affordance Grounding In this episode of Embodied AI 101, we explore "SAGA: Ground the Affordance, Then…
- Embodied AI 101
REMAC: Turning Robot Failures into Better Team Plans
REMAC: Self-reflective and self-evolving multi-agent collaboration for long-horizon robot manipulation In this episode of Embodied AI 101, we explore "REMAC…
- Embodied AI 101
Restore the Sensor Contract: Training-Free Robustness for Brittle VLA Policies
Vision–Language–Action (VLA) policies remain brittle under modest distribution shift. On LIBERO-Plus, contemporary models that solve clean tasks at high rates…
- Embodied AI 101
Before the Robot Touches Anything: Physics-Grounded Deliberation with Embodied Tree of Thoughts
Embodied Tree of Thoughts: Deliberate Manipulation Planning With Embodied World Model In this episode of Embodied AI 101, we explore "Before the Robot Touches…
- Embodied AI 101
MemoAct: Why Robot Memory Needs Both a Scratchpad and an Archive
MemoAct: Atkinson–Shiffrin-Inspired Hierarchical Memory-Augmented Policy for Robotic Manipulation In this episode of Embodied AI 101, we explore "MemoAct: Why…
- Embodied AI 101
When Touch Means Something: OmniVTLA and the Missing Contact Layer in Robot Foundation Models
OmniVTLA: Vision-Tactile-Language-Action Models With Semantic-Aligned Tactile Sensing In this episode of Embodied AI 101, we explore "When Touch Means…
- Embodied AI 101
Gaussians as State, Futures as Supervision: Inside GSP-3D
Introduction Learning robust visuomotor policies for bimanual manipulation remains challenging due to the stringent requirements for precise coordination…
- Embodied AI 101
SIRModel and the Missing Geometry Layer in Vision–Language Manipulation
Long-horizon robotic manipulation requires a policy to bridge task-level semantic reasoning with metric three-dimensional interaction geometry. Existing…
- Embodied AI 101
The Robot That Knows When to Think Twice: Inside τ0-VLA
Long-horizon robot manipulation requires a robot to both execute individual skills reliably and sequence them coherently over extended tasks. Most hierarchical…
- Embodied AI 101
When Should a Robot Stop Imagining? RISE and Adaptive World-Action Planning
Introduces dynamic Roll/Stop decision-making in world action models that adapts imagination based on planning benefit, risk, and compute cost for more…
- Embodied AI 101
The Crib as a Causal Laboratory: How a Playing iCub Learns "I Did That"
Learning sensorimotor contingencies—that is, the link between one's actions and their sensory effects—is fundamental to developing body knowledge…
- Embodied AI 101
Feeling Around Corners: STRP Compresses Fingertip Force into Directional Touch
High-voltage grid maintenance and chemical processing demand dexterous manipulation in confined, visually occluded environments. Existing teleoperation systems…
- Embodied AI 101
Give the Swarm a Pheromone Compass
Search-and-rescue (SAR) operations in disaster environments require drone swarms to coordinate efficiently despite incomplete information and potential…
- Embodied AI 101
When Every Weld Looks the Same: Language-Labeled Graphs for Industrial Localization
Abstract As industries increasingly move towards automation, the ability to visually localize objects and accurately estimate their pose, including rigid and…
- Embodied AI 101
When Robot Eyes Converge: Active Stereo for Humanoid Depth
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…
- Embodied AI 101
Two Clocks for Touch: How T-Rex Gives Dexterous VLAs a Reflex Arc
Introduces a full-stack co-designed tactile-reactive policy with asynchronous mixture-of-transformers (slow visuomotor planning + fast 4× tactile corrections)…
- Embodied AI 101
Let the VLM Decide, Let the Planner Drive: Inside Senna
Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving In this episode of Embodied AI 101, we explore "Let the VLM Decide, Let the…
- Embodied AI 101
Seeing the Nearly Invisible: Stereo Vision for Cable-Safe Agricultural UAVs
This study develops a binocular stereo vision system to enhance the operational safety of agricultural unmanned aerial vehicles (UAVs) by detecting and…
- Embodied AI 101
Taming the Flying Tip: Constrained MPC for Dynamic Deformable Linear Objects
Deformable linear objects (DLOs) exhibit highly nonlinear dynamic behavior, complicating their control during high-speed maneuvers. Furthermore, the lack of a…
- Embodied AI 101
Replay Is Not Reality: Embodied RL When Both Policy and Physics Shift
Abstract Embodied agents must continuously adapt to the physical world using interaction data collected across varying timescales, controllers, and…
- Embodied AI 101
PPO Beyond the Simulator: What a TurtleBot3 Mapless Navigation Study Really Demonstrates
Objective: In the field of mobile robotics, autonomous navigation in dynamic environments is one of the most challenging tasks in these environments…
- Embodied AI 101
VLAs in the Wild: What 142 Papers Really Say About Generalist Robot Intelligence
Generalist robots need to perform diverse tasks while operating in dynamic, uncertain, and unstructured environments, often around human beings…
- Embodied AI 101
VEDA and the Moment Vision Stops Being Enough
Industrial humanoid robots are increasingly expected to execute high-precision manipulation tasks, such as assembly, welding, and material handling, under…
- Embodied AI 101
Three Ways to Buy Back Robot Data: Augment the Demonstration, Import the Semantics, Benchmark the Physics
Abstract Robots have the potential to assist people in homes, hospitals, warehouses, and factories, but today's systems still struggle to learn robust…
- Embodied AI 101
UI-Mate: Show the Desktop Agent Once, Then Let It Replan
A 27B model that achieves one-shot doubling of strict success rates on long-horizon office automation tasks via demonstration-conditioned policy learning. In…
- Embodied AI 101
The Frozen VLM That Learns Anyway: Inside Spatial Memory Agent
A parameter-update-free framework that distills verified experience into transferable spatial memory for frozen VLMs, achieving consistent gains across 4 VLMs…
- Embodied AI 101
The LLM Proposes, the Planner Disposes: VLA-SP for Verifiable Robot Manipulation
Abstract Cross-modal foundation models are increasingly used for robotic task understanding and planning. However, connecting multimodal observations and…
- Embodied AI 101
When Vision Cannot Know: MulPlanLM Lets Robot Planners Lift, Weigh, and Decide
MulPlanLM: multimodal robotic task planning with vision-language models and physical feedback In this episode of Embodied AI 101, we explore "When Vision…
- Embodied AI 101
Reading Trust in Motion: A Multimodal Perception Stack for Industrial HRC
Abstract Industry 5.0 promotes collaborative manufacturing environments where humans and robots work together, leveraging their complementary strengths to…
- Embodied AI 101
Two MPCs, One Robot: Bézier Curves for Fast, Compliant Mobile Manipulation
Dual-arm mobile manipulators can transport and manipulate large objects with simple end-effectors. Interacting with dynamic environments subject to strict…
- Embodied AI 101
The Reward Behind the Gait: Learning Transferable Locomotion from a Few Stick-Insect Steps
Insect locomotion exhibits remarkable adaptability and flexibility despite the limited scale of its nervous system. This paper extracts and models underlying…
- Embodied AI 101
Beyond Looking Human: Bio-Functional Robot Design for Cross-Scale Dexterity and Tool Use
Dexterous hands and robotic manipulators as physical interfaces for diverse environments. Proposes bio-functional mimicry beyond structural biomimicry…
- Embodied AI 101
LDA-1B and the Case for Never Throwing Robot Data Away
Scales a latent dynamics action model to 1B parameters by ingesting diverse embodied datasets into a unified training pipeline for generalist robot policies…
- Embodied AI 101
Flex-π: One Robot Policy, Three Futures, Fifty-Six Ways to Run It
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…
- Embodied AI 101
Imagined Motion, Grounded Geometry: How Rest2Art Makes Closed Objects Articulate
An ECCV 2026 paper that converts a single static closed-state image into a simulation-ready articulated asset using video diffusion for joint hypothesis…
- Embodied AI 101
ComBodied Agents: When the Human Becomes the World Model
Proposes a new agent architecture that unifies digital and physical (embodied) action spaces, supporting human-state trajectories with explicit handling of…
- Embodied AI 101
A Sphere Every Robot Hand Can Speak
Introduces a unified hand action space that enables seamless cross-embodiment transfer for dexterous manipulation policies across different robot hands. In…
- Embodied AI 101
The Soft Gripper Pareto Frontier: Adaptability Without Surrendering Payload
Soft gripper systems have inherent flexibility and remarkable interactivity security. However, balancing load-bearing capacity, deformation, complexity, and…
- Embodied AI 101
Dyna-2's Million-Hour Bet: When Human Video Starts Improving Robots
Pre-trained on 1 million hours of human video, Dyna-2 demonstrates scaling laws for world-action models across four orders of magnitude on human data, with…
- Embodied AI 101
Think Deeply Only When It Matters: DySL-VLA and Action-Aware Dynamic Depth
Vision-Language-Action (VLA) models have shown remarkable success in robotic tasks like manipulation by fusing a language model's reasoning with a vision…
- Embodied AI 101
The 100,000-Hour Bet: How Xiaomi-Robotics-1 Tries to Make Robot Scaling Real
We present Xiaomi-Robotics-1, a foundational vision-language-action (VLA) model capable of (1) following diverse language instructions to perform a wide range…
- Embodied AI 101
Reason First, Move Second: A Modular Alternative to End-to-End Robot Policies
Explicit task reasoning empowering robotic manipulation In this episode of Embodied AI 101, we explore "Reason First, Move Second: A Modular Alternative to…
- Embodied AI 101
From BIM to the Build: A Multimodal Agent for Conversational Construction Robotics
A multimodal foundation model-enabled agent for human–robot collaboration in construction In this episode of Embodied AI 101, we explore "From BIM to the…
- Embodied AI 101
The Twin Watches, the QP Dodges: Predictive Safety for a UR10e
Safe human–robot collaboration remains a critical challenge in manufacturing. Traditional safety approaches, such as cages and proximity sensors, are often…
- Embodied AI 101
One Shot, Twenty Screws: Robot Vision for the Dirty Reality of Appliance Recycling
Industrial-grade robust robot vision system for screw detection and removal under uneven conditions.
- Embodied AI 101
Better Geometry at the Neural–Symbolic Boundary: DAIoU for Robot Manipulation
Distance–area–IoU fusion loss for better neuro-symbolic robot manipulation
- Embodied AI 101
REL Hand: A Mechanical Shortcut to More Human-Like Humanoid Dexterity
Design and evaluation of a tendon-and-linkage hybrid-driven humanoid dexterous hand
- Embodied AI 101
When the Appliance Becomes the API: Part–Function–State Models for Robot Planning
Appliances Describe Themselves: Part–Function–State Modeling for Appliance Manipulation Planning
- Embodied AI 101
CISMG-Nav: When a Navigation Agent Stops Forgetting the Building
CISMG-Nav: A Cross-Task Incremental Semantic Memory Graph-Driven Vision-and-Language Navigation Framework
- Embodied AI 101
ReaDy-Go: Teaching Robots to Dodge Photorealistic People in Gaussian-Splat Digital Twins
ReaDy-Go: Real-to-Sim Dynamic 3D Gaussian Splatting Simulation for Environment-Specific Visual Navigation With Moving Obstacles
- Embodied AI 101
The Action Is Missing: Turning Human Video into Robot Control
Recent progress in generalizable embodied control has been driven by large-scale pretraining of Vision-Language-Action (VLA) models. However, most existing…
- Embodied AI 101
Look Where You Mean: Gaze2Act Gives VLAs a Live Intent Channel
Vision-Language-Action (VLA) models have recently shown strong potential for robot learning by following language instructions. However, in practice, language…
- Embodied AI 101
HARP-VLA: Align the Eyes Before the Actions
Learning generalizable vision-language-action (VLA) models from large-scale human videos is promising but challenging due to cross-embodiment discrepancies in…
- Embodied AI 101
Make Geometry the Policy: GAM's Bet on 3D-Native Robot Control
Generalist robot policies must follow user instructions while reasoning about how objects, cameras, and robot actions interact in the 3D physical world. Recent…
- Embodied AI 101
π0.7: When a Robot Foundation Model Learns How to Behave
We present a new robotic foundation model, π0.7, that can enable strong out-of-the-box performance in a wide range of scenarios, following diverse language…
- Embodied AI 101
EgoHumanoid: Turning Human First-Person Experience into Humanoid Loco-Manipulation
Introduces a dataset and method for learning loco-manipulation policies directly from egocentric human videos without any robot data collection, enabling…