Chris Paxton & Michael Cho geek out over robotic papers with paper authors. <br/><br/><a href="https://robopapers.substack.com?utm_medium=podcast">robopapers.substack.com</a>
RoboPapers
Claim This Podcastby Chris Paxton and Michael Cho
5.0(2 reviews)
89 episodes
Updated Daily
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Podcast Overview
Chris Paxton & Michael Cho geek out over robotic papers with paper authors. <br/><br/><a href="https://robopapers.substack.com?utm_medium=podcast">robopapers.substack.com</a>
Language
🇺🇲
Publishing Since
8/8/2025
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Recent Episodes
July 8, 2026
Ep#89: Contact Grounded Policy
<p>Contact-rich manipulation is still very challenging for robotics. Problems like opening a jar, or in-hand reorientation of an object, require making repeated contact with different parts of a robot’s hand, and this is hard to do with pure vision. Instead, research is moving towards using tactile sensors in combination with visual policies. But what’s the best way to learn how to handle multi-point contact?</p><p>Zhengtong Xu and Yeping Wang tell us about their new work Contact-Grounded Policy (CGP). CGP predicts future robot state and tactile feedback, and predicts this into actions for a compliant robot controller so that a four- or five-finger robot hand can perform complex tasks involving precise manipulation, delicate grasping, and tool use.</p><p>To learn more, watch Episode #89 of RoboPapers, with Chris Paxton and Jiafei Duan.</p><p>Abstract</p><p>Contact-rich dexterous manipulation with multi-finger hands remains an open challenge in robotics because task success depends on multi-point contacts that continuously evolve and are highly sensitive to object geometry, frictional transitions, and slip. Recently, tactile-informed manipulation policies have shown promise. However, most use tactile signals as additional observations rather than modeling contact state or how their action outputs interact with low-level controller dynamics. We present Contact-Grounded Policy (CGP), a visuotactile policy that grounds multi-point contacts by predicting coupled trajectories of actual robot state and tactile feedback, and using a learned contact-consistency mapping to convert these predictions into executable target robot states for a compliance controller. CGP consists of two components: (i) a conditional diffusion model that forecasts future robot state and tactile feedback in a compressed latent space, and (ii) a learned contact-consistency mapping that converts the predicted robot state-tactile pair into executable targets for a compliance controller, enabling it to realize the intended contacts. We evaluate CGP using a physical four-finger Allegro V5 hand with Digit360 fingertip tactile sensors, and a simulated five-finger Tesollo DG-5F hand with dense whole-hand tactile arrays. Across a range of dexterous tasks including in-hand manipulation, delicate grasping, and tool use, CGP outperforms visuomotor and visuotactile diffusion-policy baselines.</p><p>Learn More</p><p><a target="_blank" href="https://contact-grounded-policy.github.io/">Project page: https://contact-grounded-policy.github.io/</a></p><p><a target="_blank" href="https://arxiv.org/abs/2603.05687">ArXiV: https://arxiv.org/abs/2603.05687</a></p> <br/><br/>This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://robopapers.substack.com?utm_medium=podcast&utm_campaign=CTA_1">robopapers.substack.com</a>
July 1, 2026
Ep#88: DexSkin: High-Coverage Conformable Robotic Skin for Learning Contact-Rich Manipulation
<p>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.</p><p>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 building DexSkin, showing how it’s useful for policy learning, including online reinforcement learning, and how it' can be calibrated and policies transferred across sensors. They also open sourced their code and methods for building the sensors.</p><p>To learn more, watch Episode #88 of RoboPapers now, hosted by Chris Paxton and Jiafei Duan!</p><p>Abstract</p><p>Human skin provides a rich tactile sensing stream, localizing intentional and unintentional contact events over a large and contoured region. Replicating these tactile sensing capabilities for dexterous robotic manipulation systems remains a longstanding challenge. In this work, we take a step towards this goal by introducing DexSkin. DexSkin is a soft, conformable capacitive electronic skin that enables sensitive, localized, and calibratable tactile sensing, and can be tailored to varying geometries. We demonstrate its efficacy for learning downstream robotic manipulation by sensorizing a pair of parallel jaw gripper fingers, providing tactile coverage across almost the entire finger surfaces. We empirically evaluate DexSkin's capabilities in learning challenging manipulation tasks that require sensing coverage across the entire surface of the fingers, such as reorienting objects in hand and wrapping elastic bands around boxes, in a learning-from-demonstration framework. We then show that, critically for data-driven approaches, DexSkin can be calibrated to enable model transfer across sensor instances, and demonstrate its applicability to online reinforcement learning on real robots. Our results highlight DexSkin's suitability and practicality for learning real-world, contact-rich manipulation. Please see our project webpage for videos and visualizations: <a target="_blank" href="https://dex-skin.github.io/">this https URL</a>.</p><p>Learn More</p><p><a target="_blank" href="https://arxiv.org/abs/2509.18830">ArXiV: https://arxiv.org/abs/2509.18830</a></p><p><a target="_blank" href="https://dex-skin.github.io/">Project Page: https://dex-skin.github.io/</a></p><p><a target="_blank" href="https://github.com/sdwistreich/dexskin">Github: https://github.com/sdwistreich/dexskin</a></p><p><a target="_blank" href="https://huggingface.co/datasets/swistreich/dexskin">Datasets: https://huggingface.co/datasets/swistreich/dexskin</a></p> <br/><br/>This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://robopapers.substack.com?utm_medium=podcast&utm_campaign=CTA_1">robopapers.substack.com</a>
June 18, 2026
Ep#87: MolmoAct 2: An open foundation for robots that work in the real world
<p>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.</p><p>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 it across the community, running experiments in their own labs or homes.</p><p>Haoquan Fang and Jiafei Duan tell us more. Watch Episode 87 of RoboPapers, with Michael Cho and Chris Paxton, now!</p><p>Abstract</p><p>Vision-Language-Action (VLA) models aim to provide a single generalist controller for robots, but today’s systems fall short for real-world deployment. Frontier models are closed; open-weight alternatives are tied to expensive hardware; reasoning-augmented policies pay prohibitive latency for their grounding; and fine-tuned success rates remain below the threshold for dependable use. We present MolmoAct2, a fully open action reasoning model built for practical deployment, advancing its predecessor, MolmoAct along five axes. (1) MolmoAct2 is built on top of our new Molmo2-ER, a VLM backbone specialized for spatial and embodied reasoning, trained on a 3.3M-sample corpus with a specialize-then-rehearse recipe. (2) We release three new robot datasets spanning low-to-medium cost platforms: MolmoAct2-BimanualYAM Dataset, 720 hours of teleoperated bimanual trajectories that constitute the largest open bimanual dataset to date; MolmoAct2-DROID Dataset, a quality-filtered Franka subset of DROID; and MolmoAct2-SO100/101 Dataset, a quality-filtered SO-100/101 subset. (3) We train and release MolmoAct2-FAST Tokenizer, an open-weight, open-data action tokenizer trained on millions of trajectories across five embodiments. (4) We design a new VLA architecture to graft the discrete-token VLM into the flow-matching continuous-action expert via per-layer key-value (KV) conditioning. (5) we propose MolmoAct2-Think, an adaptive-depth reasoning variant that re-predicts depth tokens only for scene regions that change between timesteps, retaining geometric grounding at a fraction of prior latency. In the most extensive empirical study of any open VLA to date, spanning 7 simulation and real-world benchmarks, MolmoAct2 outperforms strong baselines including π0.5, while Molmo2-ER surpasses GPT-5 and Gemini Robotics ER-1.5 across 13 embodied-reasoning benchmarks. We release model weights, training code, and complete training data.</p><p>Learn More</p><p><a target="_blank" href="https://allenai.org/blog/molmoact2">Project page: https://allenai.org/blog/molmoact2</a></p><p><a target="_blank" href="https://github.com/allenai/molmoact2">Code: https://github.com/allenai/molmoact2</a></p><p><a target="_blank" href="https://arxiv.org/pdf/2605.02881v1">ArXiV: https://arxiv.org/pdf/2605.02881v1</a></p><p>And check out our episode on the original MolmoAct:</p> <br/><br/>This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://robopapers.substack.com?utm_medium=podcast&utm_campaign=CTA_1">robopapers.substack.com</a>
89 total episodes available
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