Ep#86: RISE: Self-Improving Robot Policy with Compositional World Model

Using simulation to teach robots would unblock the massive scale of robotics data that seems to be missing right now. The issue is usually sim-to-real transfer; MolmoBOT aims to solve this with sufficient diversity of simulation data, so that no real data is necessary for robot deployment. It has a few parts: MolmoBot-Engine produces diverse pick-and-place demonstrations across many different simulated environments; MolmoBot-Data is a dataset of 1.7 million expert trajectories for item manipulation and pick-and-place tasks; and they train a number of VLA policies which can achieve strong performance on real-world pick-and-place tasks with zero real robot data. Rose Hendrix, Snehal Jauhri, Abhay Deshpande, and Maya Guru join us to give us the details. To learn more, watch RoboPapers episode 113 with Chris Paxton and Ruijie He! Abstract A prevailing view in robot learning is that simulation alone is not enough; effective sim-to-real transfer is widely believed to require at least some real-world data collection or task-specific fine-tuning to bridge the gap between simulated and physical environments. We challenge that assumption. With sufficiently large-scale and diverse simulated synthetic training data, we show that zero-shot transfer to the real world is not only possible, but effective for both static and mobile manipulation. We introduce MolmoBot-Engine, a fully open-source pipeline for procedural data generation across robots, tasks, and diverse simulated environments in MolmoSpaces. With it, we release MolmoBot-Data, a dataset of 1.7 million expert trajectories for articulated object manipulation and pick-and-place tasks. We train three policy classes: MolmoBot (a Molmo2-based VLM with a flow-matching action head), MolmoBot-Pi0 (replicating the π₀ architecture for controlled comparison), and MolmoBot-SPOC (a lightweight policy for edge deployment). Without any real-world fine-tuning, our policies achieve zero-shot transfer to unseen objects and environments, reaching 79.2% success rate on real-world tabletop pick-and-place, outperforming π₀.₅ at 39.2%. Learn More Project Page: https://allenai.github.io/MolmoBot/ ArXiV PDF: https://arxiv.org/pdf/2603.16861 Transcript Produced by ChatGPT and Riverside. Introducing MolmoBot Chris Paxton: Hey everybody, and welcome to another episode of RoboPapers. Today we’re excited to have the team behind the MolmoBot paper here to talk about their work. Would you like to introduce yourselves? Rose Hendrix: I’m Rose Hendrix. Until recently, I was a research scientist at AI2, and now I’m at a startup. Abhay Deshpande: I’m Abhay Deshpande. I conducted this work while I was a predoctoral researcher at AI2, and now I’m a PhD student at Princeton University. Maya Guru: I’m Maya Guru. I’m currently a predoctoral researcher at AI2, and I’m looking for PhD positions for this fall. Chris Paxton: Awesome. Would you like to get started and tell us a little about what you’ve been working on? Rose, maybe you can share your screen? Abhay Deshpande: I’ll pull it up. Rose Hendrix: Thanks, Abhay. On a previous episode of RoboPapers, the MolmoSpaces team came on to talk about their work, which is closely related to this. MolmoSpaces is about scaling synthetic environments and evaluation. MolmoBot is about cashing out that potential: scaling synthetic data generation, then training policies on that data to achieve zero-shot sim-to-real transfer for manipulation. MolmoBot is a data engine and a family of policies. We show that the policies transfer in large-scale simulation evaluations and in the real world, both for tabletop pick-and-place and for mobile manipulation on the RB-Y1 platform. That includes opening doors, drawers, and cabinets, as well as object manipulation and pick-and-place. Generating synthetic robot data at scale Abhay Deshpande: As Rose mentioned, we have this large framework, MolmoSpaces, for synthetic data and large-scale simulation. Within that, we built MolmoBot-Engine, a data engine

Touch is crucial for humans when they want to perform precise tasks to a high degree of reliability, and yet robots almost universally use vision — they’re often completely blind to this whole modality. Two works from Tong Jian, Tao Yu, Saleh Nabi, and Matteo Grimaldi show us how important this sense is. In SlipSense, they look at how to detect objects slipping in grippers using a compact multimodal tactile sensor, which generalizes across unseen objects and to different robots. They then talk about how tactile sensing can be used to enable high real-world performance with a challenging cable-tracing task, where a cable must be fed through the hand with a repeated pinch-and-curl motion. To learn more, watch Episode 112 of RoboPapers today, with Michael Cho and Ruijie He! SlipSense Abstract Slip detection is fundamental to dexterous manipulation, yet existing systems often lack precise characterization of detection latency and cross-platform generalization. We present SlipSense, a multimodal tactile slip-detection framework built on TacV5, a compact sensor integrating a 32×32 piezoresistive array operating at 240 Hz and a 3-axis MEMS accelerometer operating at 8 kHz. The piezoresistive array captures spatial pressure distributions, while the accelerometer captures friction-induced vibrations, providing complementary slip cues. The framework performs modality-specific encoding, intra-sensor fusion, and cross-modal attention with causal temporal prediction at 240 Hz. Experiments on a dataset of 1.4 million frames spanning 37 objects demonstrate the complementarity of the two modalities. SlipSense achieves 96.7% Macro F1 with a false-positive rate below 1.6%, detecting 76% of slip events within 23.1 ms. When trained solely on UMI data, SlipSense generalizes zero-shot to a Tesollo dexterous hand, transferring across unseen objects, distinct sensor units, and robotic platforms without retraining. Touch2Trace Abstract Dexterous manipulation of deformable objects demands continuous fingertip-level regulation of pressure, friction, and incipient slip. We study one of the most challenging cases: dexterous cable tracing, feeding a cable through the hand with repeated pinch-and-curl motions of the thumb and index finger. We introduce Touch2Trace, a tactile-driven imitation-learning system for this task, and provide, to our knowledge, the first systematic real-world characterization of how encoder pretraining, control rate, temporal context, and spatial resolution each shape policy performance. The winning learning recipe combines a tactile encoder pretrained for a custom 32 x 32 piezoresistive sensor (TacV5) via self-supervised learning with a lightweight transformer policy trained on teleoperated demonstrations via behavior cloning, deployed at 60 Hz on a Tesollo DG-5F hand. Tactile feedback without vision or explicit cable-state estimation significantly improves tracing performance versus a proprioception-only baseline: from 0.2 cm to 20.1 cm mean distance and 0% to 93% success rate, with zero-shot transfer to unseen cables and routing conditions. The results quantify the influence of key parameters in tactile-driven systems for reliable dexterous deformable object manipulation. Learn More SlipSense Project Page: https://tjian-ai.github.io/corl26-slipsense/ SlipSense on ArXiV: https://arxiv.org/abs/2609.15910 Touch2Trace on Project Page: https://grmmtt.github.io/corl26-touch2trace/ Touch2Trace on ArXiV: https://arxiv.org/abs/2609.15921 Transcript Introduction Michael Cho: Hello, everyone. Welcome to another episode of RoboPapers. Today we’re really happy to have the crew from Analog Devices, the Dexterous AI Group: Tao Yu, Tong Jian, Saleh, and Matteo. They’re coauthors of two papers that they’ll walk us through. One is Touch2Trace: Tactile-Driven Imitation Learning for Dexterous Cable Tracing, and the other is SlipSense: Multimodal Tactile Learning for Low-Latency and Generalized Slip Detection. Thank you so much for coming, guys. Mayb

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 Schroeder 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 Michael Cho and Jiafei Duan! Abstract Vision-language models (VLMs) have shown impressive capabilities across diverse tasks, motivating efforts to leverage these models to supervise robot learning. However, when used as evaluators in reinforcement learning (RL), today's strongest models often fail under partial observability and distribution shift, enabling policies to exploit perceptual errors rather than solve the task. We introduce SOLE-R1 (Self-Observing LEarner), a video-language reasoning model explicitly designed to serve as the sole reward signal for online RL. Given only raw video observations and a natural-language goal, SOLE-R1 performs per-timestep spatiotemporal chain-of-thought (CoT) reasoning and produces dense estimates of task progress that can be used directly as rewards. To train SOLE-R1, we develop a large-scale video trajectory and reasoning synthesis pipeline that generates temporally grounded CoT traces aligned with continuous progress supervision. This data is combined with foundational spatial and multi-frame temporal reasoning, and used to train the model with a hybrid framework that couples supervised fine-tuning with RL from verifiable rewards. Across four different simulation environments and a real-robot setting, SOLE-R1 enables zero-shot online RL from random initialization: robots learn previously unseen manipulation tasks without ground-truth rewards, success indicators, demonstrations, or task-specific tuning. SOLE-R1 succeeds on 24 unseen tasks and substantially outperforms strong vision-language rewarders, including Robometer, RoboReward, ReWiND, GPT-5, and Gemini-3-Pro, while exhibiting markedly greater robustness to reward hacking. We release all models, data, code, and demos at the anonymous page: this https URL Learn More Project page: https://philip-mit.github.io/sole-r1/ ArXiV: https://arxiv.org/abs/2603.28730 Transcript Transcripts are automatically generated by Riverside and are lightly edited by ChatGPT. Michael Cho [00:06] Hello guys, welcome to another episode of RoboPapers. Today we are really happy to have Philip Schroeder and Thomas Weng, two of the co-authors of this paper, SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning. Thank you so much for making the time, guys. Maybe as a start, Philip, you can share a bit about yourself and then Thomas. Philip Schroeder [00:27] Yeah, so my name is Philip. I’m a final-year PhD student at MIT in computer science. My work has mainly focused on improving the reasoning capabilities of language and vision-language models in challenging embodied settings. And over the last couple years, my work has mainly focused on reward reasoning over video, which is what we do for this paper. Thomas Weng [00:55] Cool. I’m Thomas. I’m a research scientist at RAInstitute. And more recently my work has been focusing on the pieces required to achieve like post-training and deployment for robotics. So reward modeling is one piece of that. On-robot reinforcement learning where you have data from deployment is also a piece of that. But that’s what I do. Michael Cho [01:22] All right, great to have both of you on. Yeah, maybe Philip you can tak

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, Wenli Xiao, Jia Xie, and Tonghe Zhang 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 Michael Cho, Chris Paxton, and Ruijie He! Abstract Achieving dexterous robotic manipulation in the real world heavily relies on human supervision and algorithm engineering, which becomes a central bottleneck in the pursuit of general physical intelligence. Although emerging coding agents can generate code to automate algorithm search, their successes remain largely confined in digital environments. We conjecture that the missing abstraction to automate robotics research is a repeatable feedback loop for real-world policy improvement: reset the scene, execute a policy, verify the outcome, and refine the next iteration. To bridge this gap, we introduce ENPIRE, a harness framework for coding agents that instantiates this physical feedback routine with four core modules: an Environment module (EN) for automatic reset and verification, a Policy Improvement module (PI) that launches policy refinement, a Rollout module (R) to evaluate policies with one or multiple physical robots operating in parallel, and an Evolution module (E) in which coding agents analyze logs, consult literature, improve training infrastructure and algorithm code to address failure modes. This closed-loop system transforms real-world manipulation learning into a controllable optimization procedure, minimizing human effort while allowing fair ablations across training recipe and agent variants. Powered by ENPIRE, frontier coding agents can autonomously train a policy to achieve a 99% success rate on challenging, dexterous manipulation tasks, such as organizing a pin box, fastening a zip tie, and tool use, a process that further accelerates when we dispatch an agent team on a robot fleet. Our results suggest a practical and scalable path toward deploying coding agents to autonomously advancing robotics in the physical world. Learn More Project Page: ArXiV: https://arxiv.org/abs/2606.19980 Transcript Michael Cho (00:06.088)Hello everyone, welcome to another episode of RoboPapers. Today we’re super happy to have Wenli, Jia Xie and Tonghe. They are three of the co-authors of this paper called ENPIRE: Agentic Robot Policy Self-Improvement in the Real World. Thank you so much for coming to the pod. Maybe as a start three of you can share a little bit about yourself first and then you guys can go through the paper. Wenli Xiao (00:30.821)Okay, I’ll get started. Sorry. So my name is Wenli. I’m a final-year PhD at CMU, advised by Professor Guanya Shi. Previously I did two-year internship at NVIDIA GEAR Lab with Jim Fan and Yuke Zhu. And later I joined Physical Intelligence as intern. Yeah, I did a bunch of VLA training, post-training, real-world RL and agentic stuff and now I’m mainly doing pretraining, studying the science of training and data. Yeah. Michael Cho (01:14.488)Yeah, I think just yeah. Jia Xie (01:16.463)Okay, sure. Yeah. Hi everyone. My name is Jia and I’m a master’s student at CMU, advised by Guanya. Of course we’re labmates. And I’ve interned at xDof as a research intern over the summer. And I’ve been working on some agent stuff and the extent stuff lately. So yeah, thanks for having us. Tonghe Zhang (01:43.739)Okay. Hi, my name is Tonghe. I’m also a master’s student at Professor Guanya Shi’s lab. I was doing internship at Amazon this summer and previously I’ve been working on reinforcement learning. And thank you guys for inviting us. Michael Cho (02:01.15)Alright, perfect. Great to have you guys. Yeah, Wenli, I know you have a sli

What happens if you connect a hundred AI-powered robots to the internet and just let people give them any instructions they want? Enigma founder Jonathan Jacobi 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 Paxton and Ruijie He today! Learn More Original Post on X Robots.online website (no longer active) Enigma.inc Transcript Transcript is automatically generated by Riverside and edited by ChatGPT. Chris Paxton (00:05) Hey everyone, and welcome to another episode of RoboPapers. Today we’re super excited to have Jonathan Jacobi from Enigma here to tell us about their work in foundation models and about putting a hundred robots online. So, could you introduce yourself? Jonathan Jacobi (00:19) Yeah. Thank you, Chris. Thank you, RJ. It’s a pleasure to be on RoboPapers. Big fans. A bit about Enigma: we’re a company working on foundation models for robotics, as Chris said, as well as building innovative new interfaces for robotics. How should we interact with robots? How should the interface feel to users? We’re also interested in the back-and-forth between interface work and research on the models. About a month and a half ago, we put a hundred real robots online. You could actually interact with them over the internet. They were running our models through a variety of interfaces, and you could simply ask them to do different things. So we’re here to talk about the models, the interfaces, and the experience we’ve built. Chris Paxton (01:21) Awesome. So what did people do with all these robots that you put online? Jonathan Jacobi (01:25) The basic concept was that we wanted people to feel robotics today. We all see a lot of demos. We see a lot of cherry-picked demos—some of them are cherry-picked, some of them aren’t—and we wanted people to feel that something is here right now. One of the problems is that not enough people have robots in their houses. So how do we make enough people feel that robotics is happening now, while we still have this challenge of not having enough robots deployed in the world for the average person to use? We thought quite hard about how to make that possible. One of the best ideas we came up with was: maybe we can set up a huge warehouse with a lot of robots, put our models on those robots, and let anyone online interact with them. We also got a very cool domain: robots.online. We tried to create enough different setups that one robot could be in one scenario, another robot could be in another scenario, and people would have enough flexibility to do random things that they could really feel the AI work we’ve done. They could ask the robot to do things themselves, without the logistical challenge of putting a hundred humanoids all over the world. That’s why we used tabletop robots—arms that we customized and designed ourselves, building on public open-source designs—and let people interact with them freely. Some robots were artists. People could paint. People could do pantomime. People did very unexpected things. Someone asked one of the robots holding a paintbrush to pretend that the brush was a broom. Jonathan Jacobi (03:50) So it actually swept the floor with the paintbrush. We have some very cool examples of that that we can walk through later. The concept was that, at Enigma, we focus on generalization—text, video, audio—and we have very different interfaces and input methods for our models. We wanted people to be able to experience that generalization directly and see that it’s working today. Some of the robots were artists. Some were doing chemistry experiments. We had one that could defuse a bomb. We had one that could do lightsaber-esque fighting. Not actual lightsabers. Don’t sue me. But basically, we let people do different things with different robots online. RJ He (05:01) Okay, cool. Can you describe