RoboPapers

55:05

Ep#88: DexSkin: High-Coverage Conformable Robotic...

RoboPapers ·

2026/07/01

Human skin plays an important role in how we interact with the world and robustly manipulate objects. It’s not just important when we can’t see things with out eyes, but when we want to pick up something heavy, or apply a very specific amount of force. So, it makes sense to want to give robots skin.Enter DexSkin: a soft, deformable electronic skin which can be applied across different surfaces and used to cover robot hands or fingers. Suzannah Wistreich and Baiyu Shi talk to us about their work

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62:24

Ep#87: MolmoAct 2: An open foundation for robots that...

RoboPapers ·

2026/06/18

There are few truly open models in the world, including both weights and data. However, these models are crucial for research and development of new systems — they help us learn which data is important and help develop new capabilities for deploying robots in the real world.MolmoAct2 provides a foundation for open research into robotics. It is associated with its own open dataset, an open-data action tokenizer, and a reasoning variant which predicts depth tokens. And people have actually been using

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53:46

Ep#86: RISE: Self-Improving Robot Policy with...

RoboPapers ·

2026/06/12

Robot policies must be both reliable and highly capable to be useful; the best way to achieve this level of performance is with reinforcement learning. However, for reinforcement learning you are usually stuck between two difficult options: reinforcement in the real world is often risky and expensive, while reinforcement learning in a traditional simulator takes a lot of engineering work and has a persistent sim-to-real gap. What if instead you could train your robot purely in a world model?RISE by

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61:04

Ep#85: Tutor Intelligence

RoboPapers ·

2026/06/04

Collecting robot data at scale is key to deploying working manipulation policies, and the team from Tutor Intelligence is here to tell us about how to accomplish it. Their new announcement: a massive, 100-robot “data factory,” with a behind-the-scenes look at how to build a teleoperation platform and how to make robots and policies that are useful for their customers.Tutor Intelligence is a full-stack robotics company: they build robot arms, they sell robot arms, they write the software and they

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59:14

Ep#84: Robometer: Scaling General-Purpose Robotic...

RoboPapers ·

2026/06/02

Learning robust, general-purpose reward functions for robotics unlocks many potential applications, like on-robot reinforcement learning or dataset validation. However, there’s a question of how to actually train such reward functions. Training success/failure prediction leads to ambiguous signals partway through a demonstration — it’s hard to measure progress — making the method unsuitable for reinforcement learning, among other things. Predicting progress, on the other hand, does not give a good

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82:44

Ep#83: PointWorld: Scaling 3D World Models for...

RoboPapers ·

2026/05/29

Spatial understanding is important to moving around in complex environments and is a huge part of the challenge of generalizing to new scenes. Most world models, however, largely ignore this spatial dimension, focusing on 2D images.Not PointWorld, though. PointWorld is a 3D world model trained from real and simulated data which can perform a wide variety of manipulation tasks on a real robot, including grasping or handling articulated objects, all without any additional fine tuning. Wenlong Huang

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54:37

Ep#82: SimTooReal: An Object-Centric Policy for...

RoboPapers ·

2026/05/27

Humans use tools to perform almost all of the physical work that we do from day to day. However, tools come in many different sizes and shapes, and it’s very difficult to collect human data for them in general. What about training in simulation?SimTooReal aims to address this through, unsurprisingly, sim-to-real learning. Kushal Kedia and Tyler Lum talk about how it works: they procedurally generate tool-like objects, and then train with the universal objective of moving objects around to different

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68:44

Ep#81: mimic-video: Video-Action Models for...

RoboPapers ·

2026/05/20

Robotics fundamentally involves understanding the dynamics of how things change in the world in response to action and force. This is impossible to learn from static images; instead, it’s far more effective and more data-efficient to learn from video.Elvis Nava joins us to talk about mimic-video and Mimic Robotics. Mimic-ivdeo is part of a new class of video-action models, capable of achieving complex, dexterous bimanual robotic manipulation with relatively little robot data.One of the key findings

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55:31

Ep#80: LATENT: Learning Athletic Humanoid Tennis...

RoboPapers ·

2026/05/14

Sports like tennis are great examples of the sort of dynamic whole-body interaction that’s possible with humanoid robots. But capturing examples of fast, dynamic interactions from humans is really difficult. Enter LATENT, which uses lower-quality human data plus reinforcement learning to teach a robot to play tennis, able to complete back-and-forth volleys at a human level.LATENT has three steps: (1) collecting imperfect human data like a backswing, (2) using these to learn a latent action space,

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69:11

Ep#79: Rhoda AI - Causal Video Models Are...

RoboPapers ·

2026/05/06

Training robot foundation models faces two key hurdles: how to get enough data to train an effective model, and how to make sure that new skills can be acquired quickly. The team at Rhoda AI believes that the answer is training Direct Video Action models from web data.Web data is plentiful, to the point where Rhoda can train their base model on hundreds of years of video data. And then, with the addition of robot data, they can quickly adapt it to new tasks with as little as 20 hours of in-domain

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70:46

Ep#78: Three Eras of Robot Learning

RoboPapers ·

2026/05/05

Robotics has changed dramatically over the last eight years. Ted has been involved in the cutting edge of robot learning through this period, spending those eight years at Google Brain/Google Deepmind. And he’s identified three eras of robot learning.These eras are:* The Era of Existence Proofs - trying different methods like QT-Opt, on-robot RL* The Era of Foundation Models - transitioning to data collection and clean objectives (i.e. supervised learning)* The Era of Scaling - orders of magnitude

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62:59

Ep#77: DreamDojo: A Generalist Robot World Model from...

RoboPapers ·

2026/04/29

World models have many different uses, from evaluation to training data generation to robot planning. DreamDojo is a new foundation world model that allows for impressively general and long-horizon interaction, generating coherent videos for interaction sequences over a minute long. It works in a wide range of environments and even generalizes to previously-unseen environments.We talked to Shenyuan Gao and William Liang about how they built DreamDojo, and about what tricks were necessary to scale

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48:22

Ep#76: OmniXtreme: Breaking the Generality Barrier in...

RoboPapers ·

2026/04/27

We’ve seen lots of incredible videos of humanoid robots dancing, doing martial arts, running up walls — but these extreme behaviors are usually from individual, highly specialized policies. But now OmniXtreme shows us how to achieve incredible behaviors that push the limits of humanoid motion, by (1) training a flow-based motion generative model, and (2) doing residual RL post-training to handle complex real-world dynamics.Yunsheng Wang and Shaohang Zhu join us to talk about their work towards

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61:20

Ep#75: TOPReward: Token Probabilities as Hidden...

RoboPapers ·

2026/04/23

Reinforcement on robots is highly limited by our ability to design good reward functions; this means that designing strong, generalizable reward functions is a key enabler to progress on real-world reinforcement learning.But we already have a very general class of models: VLMs. Wouldn’t it be great if you could just use a VLM to generate rewards, then? TOPReward directly generates rewards from the probability of the “True” token of a VLM question-answering response; this makes it easy to implement,

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46:59

Ep#74: Weave Robotics

RoboPapers ·

2026/04/20

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 covered building products out of end-to-end learning, the ideal form factor of a home robot, and what the important prerequisites are for deploying AI-enabled robotics in the real world.Watch epiosde #74 of RoboPapers, with Chris Paxton and Jiafei Duan,

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44:21

Ep#73: VideoManip: Dexterous Manipulation Policies...

RoboPapers ·

2026/04/18

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 be retargeted using simulation to resolve issues like collisions and interpenetration when controlling the hand.In VideoManip, Hongyi Chen and co-authors built a system to solve this problem, taking in RGB videos of humans performing manipulation tasks

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59:44

Ep#72: SONIC: Supersizing Motion Tracking for Natural...

RoboPapers ·

2026/04/15

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 100 million frames worth of data. The result: a model with a generally-useful embedding space that can be controlled by a VLA, or from human video, to perform a wide variety of humanoid whole-body-control tasks, including with zero-shot transfer to

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61:11

Ep#71: Build Your Own Robot

RoboPapers ·

2026/04/08

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, from arm control to navigation to integrating your data collection strategy into hardware design. This can make it difficult for all but the most well-funded teams to “scale” real-world robotics research.Fortunately, the team behind Build Your Own Robot

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84:53

Ep#70: A Systematic Study of Data Modalities and...

RoboPapers ·

2026/04/01

Co-training has become a key part of the recipe for training large robotics models; it means that you mix some proportion of real robot data with other data sources, like simulation or egocentric human video data. This is especially important because robotics data tends to lack diversity which can be somewhat compensated for by the inclusion of these other modalities.And yet there has not been a sizable study on what constitute good practices for cotraining until now! We talk to Fanqi Lin and Jose

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71:00

Ep#69: MolmoSpaces, an Open Ecosystem for Embodied AI

RoboPapers ·

2026/03/25

Benchmarking, evaluating, and developing robotics code is difficult, and part of this is because no simulator really reflects the diversity and scale of real embodiments. Enter MolmoSpaces from AI2: a massive open ecosystem with a range of 230,000 handcrafted and procedurally-generated home environments, including 48,000 manipulable objects. Crucially, MolmoSpaces provides simulation environments which work for both navigation and manipulation. We talked to the team: Yejin Kim, Omar Rayyan, and Max

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