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Embodied AI 101

Shaoqing Tan

Stay in the loop on research in AI and physical intelligence.

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22 episodes
Avg 32 min
English

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Yesterday · 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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Yesterday · 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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Friday · 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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Friday · 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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Thursday · 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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Thursday · 32 min
SAGA: Ground the Affordance, Then Learn the Motion
SAGA: Open-World Mobile Manipulation via Structured Affordance Grounding
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Thursday · 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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Wednesday · 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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Wednesday · 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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Tuesday · 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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Tuesday · 33 min
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
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August 31 · 33 min
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 between arms and the ability to generalize across diverse environmental conditions. Existing approach
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August 31 · 30 min
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 vision–language–action policies usually acquire geometry implicitly from visual tokens or introduce de
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August 30 · 33 min
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 vision-language-action (VLA) models make each such decision with a fixed compute budget. τ0-VLA
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August 30 · 29 min
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 efficient robot planning. Enables world action models to selectively engage imagination only when benefi
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August 29 · 29 min
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, understanding causality, and developing a sense of agency. In developmental psychology, this...
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August 29 · 31 min
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 present a severe cost–fidelity trade-off: industrial platforms exceed USD 60,000 yet lack spati
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August 28 · 29 min
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 communication failures. Existing stigmergy-based approaches provide low-bandwidth coordination but rely
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