Cover Image for Robotics & World Models Reading Club 33: Perceiving, Predicting, and Planning for Physical Interaction + AdaJEPA. SF 10/10
Cover Image for Robotics & World Models Reading Club 33: Perceiving, Predicting, and Planning for Physical Interaction + AdaJEPA. SF 10/10
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🤖 Saturday Reading Club on Robotics & World Models for AI Researchers in SF
Hosts: Junfan Zhu, Aurora Feng
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Robotics & World Models Reading Club 33: Perceiving, Predicting, and Planning for Physical Interaction + AdaJEPA. SF 10/10

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​Robotics & World Models Reading Club 33: Perceiving, Predicting, and Planning for Physical Interaction + AdaJEPA. SF 10/10

​A high-signal reading group for AI researchers & builders pushing the frontiers of robotic world models, WAMs, and embodied intelligence. In our previous sessions, we brought together researchers and engineers from Boston Dynamics, Google DeepMind, NVIDIA, Stanford, UC Berkeley, Physical Intelligence, Tesla, Generalist, Rhoda AI, and leading Bay Area robotics startups.

​Hosted by Junfan Zhu & Aurora Feng.

​Sponsored by Memories.ai.

​Support Saturday Robotics Inc: https://donate.stripe.com/28EcN52rjgeY1fJboYgEg00

​​​​​Reading Club 33's Core Theme: Perceiving, Predicting, and Planning for Physical Interaction

​Hongyu Li, Robotics Researcher at NVIDIA

​Robots that manipulate the real world must perceive contact, predict physical change, and plan under uncertainty. This talk presents my work on multimodal perception and model-based planning for generalizable robot manipulation. I first present NovaFlow, which enables zero-shot manipulation by distilling actionable 3D flow from generated video, and NovaPlan, which closes the loop on long-horizon tasks through video-language planning. I then introduce Deform360, a large-scale multi-view visuotactile dataset for deformable world models, and use it to examine the trade-offs between 2D video models and explicit 3D particle dynamics. Finally, I present Hydra-0, a generalist world model conditioned on action flow, which represents robot actions as pixel motion. This shared visual interface lets the model learn action consequences across embodiments, tasks, and environments, and supports both policy evaluation and control. Together, this work argues for physically grounded robot intelligence that unites touch, vision, world models, and planning.

​Hongyu Li is a robotics researcher at NVIDIA. His research focuses on co-designing multimodal perception and planning systems for robot manipulation, with an emphasis on learning models for environmental and object interaction. His work spans visuo-haptic perception, tactile sensing, world models, with applications to deformable object manipulation and zero-shot robot planning. During his Ph.D., he worked with Prof. George Konidaris at Brown University and Prof. Yunzhu Li at Columbia University. His research has appeared in venues including RSS, CoRL, ICRA, ECCV, IROS, and RA-L. Prior to joining NVIDIA, he interned at the Robotics and AI Institute, Amazon Robotics, and Honda Research Institute.


​Keynote 2: AdaJEPA

​Ying Wang, NYU

​JEPA world models enable planning by predicting the consequences of actions in a learned representation space. We explore how to shape this space for effective planning and how to adapt the model when its predictions diverge from reality.

​📐What is a good latent space for world modeling and planning? Inspired by the perceptual straightening hypothesis in human vision, we introduce temporal straightening to improve representation learning for latent planning. Temporal straightening encourages locally straight latent trajectories, making Euclidean distance of representations a better proxy for geodesic distance and gradient-based planning more stable and effective.

​🤖How can world models adapt when their predictions become inaccurate, especially under test-time distribution shift? We introduce AdaJEPA, an adaptive WM that plans, acts, and adapts in a closed loop. Every action leads to a new observation, and every transition refines the latent representation and prediction.

​

​Ying Wang is a PhD student at NYU, advised by Prof. Yann LeCun and Prof. Mengye Ren. Her research focuses on world models. Prior to starting her PhD, she earned an MS in Data Science at NYU and a BS in Computer Science, Statistics, and Finance at McGill University.


​Keynote 3: Ben Zhou (Memories.ai)

​We present a visual memory system for physical AI, built from two components: the Large Visual Memory Model (LVMM) and a video data lake. Together they enable robotic agents to self-improve through a data-centric loop.

​LVMM is an omni perception model built specifically for human-centric video, covering both egocentric (first-person) and exocentric (third-person) footage. It takes video and text as input and produces a unified set of outputs from a single backbone: dense embeddings for indexing and retrieval, natural-language captions, head and hand pose, and 3D understanding of the scene and the person's interaction with it. LVMM also produces decision outputs, such as judging whether a person is performing a task correctly, which moves the model from describing activity to evaluating it.

​The video data lake is infrastructure optimized end to end for video workloads, covering streaming ingestion, processing, storage, and retrieval. LVMM continuously indexes what flows into the lake, turning raw footage into structured, searchable, training-ready memory.

​On top of this system, physical agents can improve themselves in a data-centric way. They can retrieve relevant human demonstrations, detect their own failures, and find the data that targets those failures. That data then feeds back into training, so each deployment cycle makes the agent better. This shortens the path from collecting video to improving robot behavior, and positions visual memory as core infrastructure for scaling embodied intelligence..


​Lightning Talk 4: GPT-6 CUA → Robotics: The Next Real2Sim2Real?

​Tonghe Zhang (CMU)

​How CUA training in GPT-6 could directly reshape robot perception, planning, and decision-making—and unlock new possibilities for agentic Real2Sim2Real.

​But agentic robotics has a catch: where does language-model agency actually help, and where does it break in the physical world? 👀


​​​​​​​​​​​​Location

​San Francisco

​​​​​​​​​​​​​​​Date & Time

​Saturday, October 10, 2026 | 2:00 PM – 5:00 PM

​​​​​​​​​​​​​​​​Join Discord Community

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​​​​​​​​​​​​​​​Agenda

​2:00 PM – 2:30 PM Door Opens & Social

  • ​Food 😋, beverages🧋 and UNLIMITED strawberries 🍓 (our official reading club fruits ☺️😄).

​2:30 PM – 3:30 PM Keynote by

​YouTube Recording: TBD (We are looking for recording volunteers)

​3:30 PM – 5:00 PM Q&A, ​open-floor roundtable (10–20 min per topic) on spotlight papers or any paper you’d like to highlight. Feel free to share why the paper matters and its technical details.


​​​​​​​​​​​​​​​​​​​​​​Past events

​#iros-reading-club-31-0928: 🍾 IROS 2026 x Saturday Robotics — Robotics Research Night | Reading Club 30. Pittsburgh 9/28

​#reading-club-29-0919: AgiBot × ManiFormer: Building the Next Generation of Physical AI. SF 9/19

​#reading-club-28-0912: Booster T2: The Next Frontier of Open Humanoid Robotics. SF 9/12

​#reading-club-26-0905: Rethinking Robot Development: Co-Designing Morphology, Sensing, and Learning

​Chenyang Ma (Applied Intuition, UNC)

​#reading-club-25-0829: Sunnyvale 8/29

​#reading-club-24-0822: Contact-Rich Robot Learning from Human Videos and Tactile. SF 8/22

​Kelin Yu (Maryland, Amazon FAR)

​#reading-club-23-0815: Engineering Robotic Simulators for Evaluation and Beyond. SF 8/15

​Kaifeng Zhang (Columbia, World Labs)

​#reading-club-22-0808: ODEWorld: A Continuous Predictive Architecture via Physical-Time Flow. SF 8/8

​Haoyi Niu (UC Berkeley)

​#reading-club-21-0801: Vision-Language-Kinematics Supervision for Perception-Based Humanoid Loco-Manipulation — SF 8/1

​Yen-Jen Wang (UC Berkeley, Amazon FAR)

​#reading-club-20-0725: Agentic Robotics Models (ENPIRE and Cap-X), Mountain View 07/25

​Haoru Xue (UC Berkeley)

​Kris Hauser (Samsung Research America, Robot Intelligence Lab)

​#SIGGRAPH-reading-club-19-0722: SIGGRAPH x Saturday Robotics — World Models for Robotics: Bridging Graphics, Simulation & Physical Intelligence | Reading Club 19, LA 07/22

​#reading-club-18-0718: Causal World Models For Real-World Intelligence. SF 07/18

​Guanming Wang & Bill (General Instinct, YC P26)

​Feng Fan (UCSD, Aether AI)

​#reading-club-17-0711: Soft Tactile-Centric Multimodal Intelligence Toward Safe and Dexterous Manipulation. SF 07/11

​Quan Luu, Purdue.

​#private-lunch-icml-0709: Saturday Robotics x ICML Private Lunch (Seoul)

​#reading-club-16-0704: The Embodied AI Hardware Stack — Supply Chain, Sensors, and the Data Flywheel — SF 07/04

​Jerry Huang, Robotics Center of Silicon Valley.

​#reading-club-15-0627: Scaling Touch: Flexible Tactile Skin for Dexterous Manipulation

​Binghao Huang, Columbia, Amazon FAR.

​#deep-tech-week-14-0625: Deep Tech Week Research Night

​SPEAR: A Simulator for Photorealistic Embodied AI Research. (ECCV 2026 accepted) by Mike Roberts, Senior Research Scientist, Adobe Research.

​Bogdan Cristei, Venture Partner at SHACK15 Ventures.

​Simone Totaro, CTO at Saturn Dynamics.

​Shumo Chu, CEO at General Intelligence Labs.

​Margaret Zhang, CEO at ThirdBrain Labs.

​#reading-club-13-0620: HumanEgo: Train Robot Policy from 30 min Egocentric Videos — SF 0620

​#reading-club-12-0613: Origami Robotics (YC W26) on Dexterity

​#cvpr-denver-11-0606: 🤖🥘 Saturday Robotics x Manycore Tech x Neural Motion | CVPR 2026 Denver Research Night | Robotics & World Models Reading Club 11

​Junfan Zhu & Aurora Feng, Founders of Saturday Robotics

​Anthony Zhao, Head of North America at Manycore Tech SpacialVerse

​Aurora Feng, Founder at Neural Motion. NM-GenET.

​Max Zhaoshuo Li, Robotics and World Model Tech Lead at NVIDIA Cosmos. Cosmos 3.

​Xiaofan Li, World Model Tech Lead at X Square Robot. WALL-WM.

​Zesen Zhao, University of Michigan. Test-Time Scaling for World Action Models via Zero-Shot Geometric Verification.

​Pengyi Liao. VGGT-Ω: From 3D Reconstruction to Scalable Spatial Representation.

​Jie Wang, University of Pennsylvania, GRASP Lab. Toward a Robotics MMLU: Lessons from Sim & Real Evaluations of Generalist Policies.

​Gordon Qian, Senior AI Researcher at Snap. Diffusion-DRF: Free, Rich, and Differentiable Reward for Video Diffusion Fine-Tuning.

​#reading-club-10-0530: Bringing Robots to Life — Learning Humanoid Instincts from the Body Up | San Francisco 0530

​Haochen Shi (Stanford, co-advised by Karen Liu & Shuran Song)

​#private-dinner-01-0529: Robo Plov x Saturday Robotics

​#reading-club-09-0523: CVPR Warm-up & Founders Spotlight — DeltaWorld + VisuoTactile Dexterous Hands

​Tommie Kerssies (Amazon Frontier AI & Robotics)

​Arjun Subramaniam (Factory Intelligence)

​#reading-club-08-0516: Embodied Human Data as the “Internet of Motion and Behavior”

​Ryan Punamiya (NVIDIA Gear, Georgia Tech)

​#reading-club-07-0509: Learning to Dream: World Models, Imagination, Path to Foundation Models for Control

​Ahmet Şemi ASARKAYA (Agility Robotics)

​#reading-club-06-0502: Evolution of Video World Models for Robotics

​Tongzhou Mu (Rhoda AI)

​#reading-club-05-0425: World Models for Physical Intelligence: From Predictive Brains to Embodied Robots

​Daniel Dugas & Sergio Arnaud (Meta FAIR)

​#reading-club-04-0418: Abstractions of the Physical World for Decision-Making

​Siming He (UC Berkeley)

​#reading-club-03-0411: Robotic Policy Adaptation

​Haoyi Niu (UC Berkeley)

​#reading-club-02-0404: JEPA Zoo

​Julian Saks (/JulianSaks)

​#reading-club-01-0328

​Join Discord Community

​Join Discord Server

​Follow Saturday Robotics

​/saturdayrobotic

​Follow YouTube

​/saturdayrobotic

​Subscribe to Luma Calendar

​https://luma.com/saturdayrobotic

Location
Please register to see the exact location of this event.
San Francisco, CA
Avatar for Saturday Robotics
Presented by
Saturday Robotics
🤖 Saturday Reading Club on Robotics & World Models for AI Researchers in SF
Hosts: Junfan Zhu, Aurora Feng
discord.gg/WH7DrTHRXK
103 Went