We've hit 100 episodes! Here's a look back on our journey so far (website with some stats): robopapers100.com/ Some highlights in the thread 🧵:

Full episode dropping soon! Geeking out with @c_voelcker, @ArpitBahety, and @zhiyuan_zhou_ on OopsieData, a community-driven effort to build the first large-scale dataset of real robot manipulation failures oopsie-data.com Co-hosted by @chris_j_paxton and @DJiafei

Full episode dropping soon! Geeking out with @rosemhendrix, @SnehalJauhri, Abhay Deshpande, and Maya Guru on MolmoB0T: Large-Scale Simulation Enables Zero-Shot Manipulation allenai.github.io/MolmoBot/ Co-hosted by @chris_j_paxton and @ruijie_sg

Full episode dropping soon! Geeking out with @rosemhendrix, @SnehalJauhri, Abhay Deshpande, and Maya Guru on MolmoB0T: Large-Scale Simulation Enables Zero-Shot Manipulation allenai.github.io/MolmoBot/ Co-hosted by @chris_j_paxton and @ruijie_sg

Full episode dropping soon! Geeking out with team with Tong Jian, Tao Yu, Saleh Nabi, Matteo Grimaldi on SlipSense: Multimodal Tactile Learning for Low-Latency and Generalized Slip Detection & Touch2Trace: Tactile-Driven Imitation Learning for Dexterous Cable Tracing tjian-ai.github.io/corl26-sl… grmmtt.github.io/corl26-touc… Co-hosted by @micoolcho @ruijie_sg

Full episode dropping soon! Geeking out with team with Tong Jian, Tao Yu, Saleh Nabi, Matteo Grimaldi on SlipSense: Multimodal Tactile Learning for Low-Latency and Generalized Slip Detection & Touch2Trace: Tactile-Driven Imitation Learning for Dexterous Cable Tracing tjian-ai.github.io/corl26-sl… grmmtt.github.io/corl26-touc… Co-hosted by @micoolcho @ruijie_sg

Reinforcement learning is often seen as key to achieving scalable, highly reliable real-world robot performance, yet it requires good reward functions that can supervise the wide range of tasks that need to be performed. The obvious thing to try would be a large vision-language model (VLM), but these models often fail under common real-world conditions like partial observability and occlusion. As a solution @Philip_MIT and @thomas_weng propose SOLE-R1, a video-language reasoning model trained to serve as a reward signal for online RL. Given real video, SOLE-R1 will reason over input video frames, generates reasoning traces, and predicts a reward signal. This allows online RL from random initialization on new manipulation tasks. To learn more, watch Episode 111 of RoboPapers, with @micoolcho and @DJiafei!

Full episode dropping soon! Geeking out with @Philip_MIT @thomas_weng on SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot RL philip-mit.github.io/sole-r1… Co-hosted by @micoolcho @DJiafei

Full episode dropping soon! Geeking out with @Philip_MIT @thomas_weng on SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot RL philip-mit.github.io/sole-r1… Co-hosted by @micoolcho @DJiafei

In many fields, automated research has become increasingly plausible with the improving capabilities of frontier language models, and the field robotics may be no exception. In ENPIRE, @_wenlixiao, @jiaxie_jason, and @TongheZhang01 build a harness framework that captures physical feedback and allows for automated robotics research, minimizing human effort while training policies that achieve a 99% success rate on challenging, dexterous tasks like organizing a pin box or fastening a zip tie. Learn more in Episode 110 of RoboPapers, with @micoolcho, @chris_j_paxton and @ruijie_sg!

Full episode dropping soon! Geeking out with @_wenlixiao @jiaxie_jason @jiaxie_jason on ENPIRE: Agentic Robot Policy Self-Improvement in the Real World research.nvidia.com/labs/gea… Co-hosted by @micoolcho @chris_j_paxton @ruijie_sg

Full episode dropping soon! Geeking out with @_wenlixiao @jiaxie_jason @jiaxie_jason on ENPIRE: Agentic Robot Policy Self-Improvement in the Real World research.nvidia.com/labs/gea… Co-hosted by @micoolcho @chris_j_paxton @ruijie_sg

What happens if you connect a hundred AI-powered robots to the internet and just let people give them any instructions they want? @Enigma_AI founder @j0nathanj joins us to talk about his experience deploying a fleet of robots on the internet, keeping them operational, and watching people truly test the generality and robustness of frontier robotics models. Watch Episode 109 of RoboPapers with @chris_j_paxton and @ruijie_sg today!

Full episode dropping soon! Geeking out with @j0nathanj on @Enigma_AI Co-hosted by @chris_j_paxton @ruijie_sg

Pretrained vision-language-action models for robots often fail when a task is performed in a slightly different environment. With Vision-Language Steering, you can get better generalization performance out of a frozen robot policy, by steering the sampling process of your pretrained diffusion or flow-matching policy to handle out-of-distribution changes like obstacle position or changing visual appearance. @liu_shuo42927 and @ishneet0710join us to talk about how to better generalize robot policies with no new training data. Watch Episode 108 of RoboPapers today, with @ruijie_sg and @DJiafei!

Full episode dropping soon! Geeking out with @j0nathanj on @Enigma_AI Co-hosted by @chris_j_paxton @ruijie_sg

Full episode dropping soon! Geeking out with @liu_shuo42927 @ishneet0710 on VLS: Steering Pretrained Robot Policies via Vision–Language Models vision-language-steering.git… Co-hosted by @ruijie_sg @DJiafei

Full episode dropping soon! Geeking out with @liu_shuo42927 @ishneet0710 on VLS: Steering Pretrained Robot Policies via Vision–Language Models vision-language-steering.git… Co-hosted by @ruijie_sg @DJiafei

Achieving human-like dexterity is the next frontier for robotics, and yet dexterity data is often subtly hard to scale. Real-world dexterity data, including things like finger-pose estimates, is often slightly off, making it physically invalid and hard to execute on real hardware and hard to learn from. DO AS I DO is an algorithm for reconstructing and retargeting monocular RGB videos to robot hands, outperforming the state of the art and even working from generated videos. @bhawna_paliwal_ @HarithejaE @willjhliang and @notmahi join us to tell us more. Watch Episode 107 of RoboPapers, with @chris_j_paxton and @DJiafei, now!

Full episode dropping soon! Geeking out with @bhawna_paliwal_ @HarithejaE @willjhliang @notmahi on Do as I Do: Dexterous Manipulation Data from Everyday Human Videos do-as-i-do.com/ Co-hosted by @chris_j_paxton @DJiafei

Full episode dropping soon! Geeking out with @bhawna_paliwal_ @HarithejaE @willjhliang @notmahi on Do as I Do: Dexterous Manipulation Data from Everyday Human Videos do-as-i-do.com/ Co-hosted by @chris_j_paxton @DJiafei