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Embodied AI 101

Shaoqing Tan

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30 episodes
Avg 32 min
English

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Yesterday · 33 min
W²G-Net: Carrying Instance Identity from Shiny Scrap to the Gripper
Purpose This study aims to develop a robust vision-guided framework for instance-level recognition and grasp-based classification of metal waste in dense, multi-scale and highly reflective industrial environments, addressing the challenges...
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Yesterday · 33 min
When Avoidance Is Not Enough: T-CARE Gives UAV Swarms a Right-of-Way
Multi-agent UAV navigation in cluttered environments is challenged by deadlock, starvation-like persistent yielding, oscillation, and unsafe congestion, particularly in narrow passages and dynamically emerging bottlenecks. Existing approaches often rely o
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Wednesday · 31 min
Teaching Robots What "Better" Means
A method for preference-based learning applied to dexterous robotic manipulation, enabling robots to learn from human preferences in freeform settings. Discussed publicly around September 8, 2026.
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Wednesday · 29 min
TANGO: Stop Treating a Humanoid Like a Disk on the Floor
TANGO directly predicts 29-DoF joint actions from RGB observations for zero-shot real-world humanoid navigation, enabling coordinated arms, torso, and gait control in cluttered 3D spaces. Presented at CoRL 2026.
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Tuesday · 31 min
Stop Teaching the VLA Your Camera: Robot-Centric Pointmaps as an Action-Aligned Visual Interface
Vision-language-action (VLA) models predict robot actions from visual observations and language instructions. These actions are defined in the robot's own 3D coordinate frame, yet most VLAs observe the scene in 2D. This paper proposes robot-centric pointm
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Tuesday · 34 min
One Policy, Many Bodies: Qwen-VLA's Bid to Unify Embodied AI
Embodied intelligence is often studied through specialized models for individual tasks such as manipulation or navigation, resulting in fragmented capabilities and limited generalization across tasks, environments, and robot embodiments.
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Monday · 30 min
When the Simulator Lies About Friction: Physics as a Transfer Prior for Touch
Physics-Regularized Learning for Robust Sim-to-Real Tactile Friction Estimation
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Monday · 33 min
Seeing Around Corners, Acting at the Edge: REACT as a VLA Safety Co-Pilot
Edge-based multimodal sensor data fusion with Vision-Language-Action (VLA) model for real-time autonomous vehicle accident avoidance
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Sunday · 30 min
RoboTok: Turning the Internet Into a Motion Search Engine for Dexterous Robots
RoboTok retrieves human hand-pose trajectories from internet videos to automatically generate scalable robot training data for dexterous manipulation tasks. This approach enables large-scale data collection without manual annotation or teleoperation.
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Sunday · 34 min
VLAct: The VLA Backbone Is Not Just Plumbing
VLAct proposes a representation-centric continued pre-training approach for Vision-Language-Action models that converts limited robot data into transferable action knowledge, achieving 92.5% success on RoboTwin with only 20% of full training data. The met
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Saturday · 33 min
Two Models, One Gradient: Making World-Model RL Work for Contact-Rich Robots
Breaks intractable full world-model RL into a heavy global trajectory model combined with a lightweight latent local dynamics approximator, enabling scalable RL for contact-rich humanoid skills without backpropagating through the full model.
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Saturday · 34 min
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.
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September 4 · 34 min
Geometry Is the Contact Plan: Bringing Surface Reasoning Into MPC
Geometry-Informed Contact Optimization and Model Predictive Control for Efficient Contact-Rich Manipulation
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September 4 · 30 min
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
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September 3 · 30 min
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 Reinforcement Learning over a frozen World Model. RP-1 reimagines planning as a learned policy: star
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September 3 · 32 min
SAGA: Ground the Affordance, Then Learn the Motion
SAGA: Open-World Mobile Manipulation via Structured Affordance Grounding
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September 3 · 31 min
REMAC: Turning Robot Failures into Better Team Plans
REMAC: Self-reflective and self-evolving multi-agent collaboration for long-horizon robot manipulation
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September 2 · 31 min
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 can fall below 30% success when the camera's viewpoint or background changes. This paper propose
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September 2 · 31 min
Before the Robot Touches Anything: Physics-Grounded Deliberation with Embodied Tree of Thoughts
Embodied Tree of Thoughts: Deliberate Manipulation Planning With Embodied World Model
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September 1 · 34 min
MemoAct: Why Robot Memory Needs Both a Scratchpad and an Archive
MemoAct: Atkinson–Shiffrin-Inspired Hierarchical Memory-Augmented Policy for Robotic Manipulation
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