Rephonic
Artwork for RoboPapers
Robotics
Vision-Language Models
Project Instinct
Sim-To-Real
General Intuition
Attention-Based Map Encoding For Learning Generalized Legged Locomotion
Maniflow
Unifp
Force Sensing
Humanoid Everyday
Imitation Learning
Dreamzero
Molmospaces
Diamond
World Models
Semantic World Models
Gaussian Splatting
World Action Models
Robot Manipulation
Gen2act

Chris Paxton & Michael Cho geek out over robotic papers with paper authors. robopapers.substack.com

PublishesTwice weeklyEpisodes74Founded5 months ago
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Latest Episodes

Do you want to never fold clothes again? Weave is a robotics startup founded in early 2024, aiming to build useful home robots as a product. We talked with co-founder Kaan Doğrusöz, and learned about his journey building a home robotics startup. We c... more

Teaching robots to perform dexterous manipulation tasks currently requires teleoperation, which limits demonstration quality, speed, and scalability. Instead, why not use human videos? The problem is that a human hand isn’t a robot hand, so data must... more

How can we build a general-purpose “foundation model” for robot motion? Zhengyi Luo joitns us to talk about SONIC, which uses motion tracking as a foundational task for humanoid robot control, and scales humanoid control training to 9k GPU hours and ... more

Robots, unfortunately, tend to be expensive. And finding a robot that’s both capable of performing a wide variety of mobile manipulation tasks, and is affordable and “hackable”, is extremely difficult. Many different problems need to be addressed, fr... more

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Recent Guests

Jose Barreiros
Senior Scientist / Tech Lead at TRI (Utah Research Institute)
TRI (Utah Research Institute)
Episode: Ep#70: A Systematic Study of Data Modalities and Strategies for Co-training Large Behavior Models for Robot Manipulation
Fanqi Lin
PhD student at Tsinghua University; intern at TRI
Tsinghua University; TRI
Episode: Ep#70: A Systematic Study of Data Modalities and Strategies for Co-training Large Behavior Models for Robot Manipulation
Yejin Kim
Research engineer leading MolmoSpaces project
Allard Institute for AI
Episode: Ep#69: MolmoSpaces, an Open Ecosystem for Embodied AI
Omar Rayyan
First-year PhD student working on MolmoSpaces
UCLA / AI2
Episode: Ep#69: MolmoSpaces, an Open Ecosystem for Embodied AI
Max Argus
Postdoctoral researcher focused on computer vision for robots
AI2
Episode: Ep#69: MolmoSpaces, an Open Ecosystem for Embodied AI
Seonghyeon Ye
Co-author of DreamZero, PhD student at KAIS, research intern at NVIDIA
KAIS, NVIDIA
Episode: Ep#68: DreamZero: World Action Models are Zero-Shot Policies
Jianke Zhang
First author of VLM4Vla, PhD student at Tsinghua University
Tsinghua University
Episode: Ep#65: VLM4VLA: Revisiting Vision-Language Models in Vision-Language-Action Models
Shaoting Zhu
PhD student and researcher on Project Instinct
Project Instinct / Instinct Lab
Episode: Ep#64: Project Instinct
Hongyu Li
Co-author, Brown PhD student, co-author on NovaFlow
Brown University/RAI Robotics and AI Institute
Episode: Ep#63: NovaFlow: Zero-Shot Manipulation via Actionable Flow from Generated Videos

Hosts

Chris Paxton
Host of RoboPapers (co-host)
Michael Cho
Host of RoboPapers (co-host)
Jiafei
Host/Questioner; appears as guest-host in some episodes

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Apple Podcasts
#67
Israel/Technology
Apple Podcasts
#136
Switzerland/Technology
Apple Podcasts
#199
Poland/Technology

Talking Points

Recent interactions between the hosts and their guests.

Ep#53: Semantic World Models
Q: How are the questions about the future generated for planning?
Currently the questions are manually specified for each task to guide the world model toward the desired goals; the authors acknowledge it would be beneficial to automate this in future work, possibly deriving questions from goal specifications.
Ep#53: Semantic World Models
Q: How are these candidate actions being generated?
Candidate actions are generated by two planning methods: a sampling-based approach that initializes random actions and an improvement-based method that refines a base policy's actions using the world model to produce better trajectories.
Ep#45: HERMES: Human-to-Robot Embodied Learning From Multi-Source Motion Data for Mobile Dexterous Manipulation
Q: How does the reward design support multi-object manipulation and exploration?
The most important reward is the object-distance-chain, aligning hand-relative vectors to the object center, plus a hand-pose reward to accelerate exploration; penalties and smoothness terms help stabilize learning, enabling effective multi-object tasks beyond simple object tracking.
Ep#45: HERMES: Human-to-Robot Embodied Learning From Multi-Source Motion Data for Mobile Dexterous Manipulation
Q: What is the data mix across mocap, video, and teleoperation, and how much data of each source did you use?
They used three data types with one-shot demonstrations for teleoperation-generated data, mocap data from UpToShare/Orking datasets, and self-recorded RGB videos for hand/object trajectories; importantly, they emphasize they did not mix data sources for a single task but used one motion trajectory per task, with emphasis on one-shot data and augmentation in simulation.
Ep#44: From Pixels to Predicates: Learning Symbolic World Models via Pretrained Vision-Language Models
Q: How does the robot decide which operator to use at test time?
The system is given a goal and searches over a learned pool of operators, selecting a sequence that achieves the goal by chaining predicates; the process is non-differentiable and relies on hill-climbing style optimization over predicates and operators.

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Frequently Asked Questions About RoboPapers

What is RoboPapers about and what kind of topics does it cover?

Technical deep dives into robotics research papers, simulation benchmarks, and open hardware/software projects. Across recent episodes, the show spotlights embodied AI, world models, sim-to-real transfer, and hands-on robotics systems, with guests ranging from university researchers to industrial labs. Discussions emphasize practical engineering challenges, data strategies, and open ecosystems, often exploring how new papers translate into runnable pipelines, benchmarks, and hardware implementations. The format tends to blend theory with hands-on demonstrations, and notable recurring themes include scalable simulation platforms, zero-shot or few-shot policy transfer, and community-driven benchmarking. This mix is likely to appeal to enginee... more

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RoboPapers launched 5 months ago and published 74 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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What guests have appeared on RoboPapers?

Recent guests on RoboPapers include:

1. Jose Barreiros
2. Fanqi Lin
3. Yejin Kim
4. Omar Rayyan
5. Max Argus
6. Seonghyeon Ye
7. Jianke Zhang
8. Shaoting Zhu

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