RoboPapers

@chris_j_paxton, @micoolcho & @DJiafei geeking out weekly with authors of robotics AI papers. On YouTube / X / Spotify / Substack

[RoboPapers @RoboPapers]

3 of us @micoolcho @chris_j_paxton @DJiafei are super excited to help organize the Robotic Origami Competition at IROS (Sept 2026), along with @BitRobotNetwork @SharpaRobotics @LightwheelAI @hq_fang @sanatem @Noriaki_Hirose @gao_young

Calling for teams!

[RoboPapers @RoboPapers]

Full episode dropping soon!

Geeking out with @joshgruenstein @JesseMMichel @shirazkn Joe McCalmon on @tutorintel

Co-hosted by @chris_j_paxton @DJiafei

[RoboPapers @RoboPapers]

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 way of using failure data.

So why not do both? Robometer combines both progress and preference supervision, resulting in a stable, scalable, and highly general reward learning approach. @aliangdw @yigitkkorkmaz
and @Jesse_Y_Zhang join us to tell us more.

Watch Episode #84 of RoboPapers, with Chris Paxton and Jiafei Duan today!

[RoboPapers @RoboPapers]

Can't wait to push the boundary of dexterous manipulation with our collaborators: Nippon Origami Association @BitRobotNetwork @LightwheelAI @SharpaRobotics @sanatem @hq_fang @Noriaki_Hirose @gao_young

[RoboPapers @RoboPapers]

3 of us @micoolcho @chris_j_paxton @DJiafei are super excited to help organize the Robotic Origami Competition at IROS (Sept 2026), along with @BitRobotNetwork @SharpaRobotics @LightwheelAI @hq_fang @sanatem @Noriaki_Hirose @gao_young

Calling for teams!

[RoboPapers @RoboPapers]

Interested to learn more?

Register for our info sessions here: https://t.co/exr94qzqWF

[RoboPapers @RoboPapers]

Full episode dropping soon!

Geeking out with @joshgruenstein @JesseMMichel @shirazkn Joe McCalmon on @tutorintel

Co-hosted by @chris_j_paxton @DJiafei

[RoboPapers @RoboPapers]

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 joins us to tell us more about what makes this work and how it’s different from other world models.

Watch Episode #83 of RoboPapers, with @chris_j_paxton and @DJiafei, to learn more!

[RoboPapers @RoboPapers]

Full episode dropping soon!

Geeking out with @aliangdw @yigitkkorkmaz @JiahuiZhang__32 on Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons https://t.co/P8OXU1ziXY

Co-hosted by @chris_j_paxton @DJiafei

[RoboPapers @RoboPapers]

Full episode dropping soon!

Geeking out with @aliangdw @yigitkkorkmaz @JiahuiZhang__32 on Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons https://t.co/P8OXU1ziXY

Co-hosted by @chris_j_paxton @DJiafei

[RoboPapers @RoboPapers]

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. @kushalk_ and @tylerlum23 talk about how it works: they procedurally generate tool-like objects, and then train with the universal objective of moving objects around to different locations. This creates a general-purpose model which can manipulate various tools to perform a variety of tasks in the real world.

Watch episode #82 of RoboPapers, hosted by @micoolcho and @DJiafei, now to learn more!

[RoboPapers @RoboPapers]

Full episode dropping soon!

Geeking out with @wenlong_huang on PointWorld: Scaling 3D World Models for In-The-Wild Robotic Manipulation https://t.co/fEI6hbjKqL

Co-hosted by @chris_j_paxton @DJiafei

[RoboPapers @RoboPapers]

Full episode dropping soon!

Geeking out with @wenlong_huang on PointWorld: Scaling 3D World Models for In-The-Wild Robotic Manipulation https://t.co/fEI6hbjcBd

Co-hosted by @chris_j_paxton @DJiafei

[RoboPapers @RoboPapers]

Full episode dropping soon!

Geeking out with @kushalk_ @tylerlum23 on SimToolReal: An object-centric policy for Zero-Shot Dexterous Tool Manipulation https://t.co/XHAXHrMHo2

Co-hosted by @micoolcho @DJiafei

[RoboPapers @RoboPapers]

Full episode dropping soon!

Geeking out with @kushalk_ @tylerlum23 on SimToolReal: An object-centric policy for Zero-Shot Dexterous Tool Manipulation https://t.co/XHAXHrNfdA

Co-hosted by @micoolcho @DJiafei

[RoboPapers @RoboPapers]

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.

@elvisnavah joins us to talk about @mimicrobotic. One of the key findings from mimic-video is that pretraining on webscale video allows robots to learn physics priors; as a result, policies train faster, generalize better, and are capable of more impressive dexterity, versus training on static images or image-language pairs as per a VLM.

Watch Episode #81 of RoboPapers with @micoolcho and @chris_j_paxton to learn more!