# Robotic manipulation Published articles for Robotic manipulation. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Amazon and University of Michigan give robots a sense of touch DevFeed: [Amazon and University of Michigan give robots a sense of touch]() Original publisher: [Read original article]() Author: Mani Nambi; Nima Fazeli Published: 2026-07-10T17:13:31Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [contact-rich manipulation]() Tags: [amazon](), [amazon-robotics-research](), [contact-rich-manipulation](), [dataset-development](), [dexterous-robot-manipulation](), [gelsight-mini-sensor](), [hydroelastic-contact-model](), [hydroshear-simulator](), [physics](), [reinforcement-learning](), [reinforcement-learning-robotics](), [robot-grasping-simulation](), [robot-sense-of-touch](), [robotic-manipulation](), [robotics](), [robots](), [sim-to-real-transfer-robotics](), [training]() ### AI overview HydroShear is a physics-based tactile-force simulator that trains robots for dexterous, contact-rich manipulation in simulation and transfers the resulting policies to real-world tasks. ### Source excerpt HydroShear, a new physics-based simulator, teaches robots how to use their sense of touch to perform complex manipulation tasks, in a way that transfers seamlessly to the real world. ## Pollen-Vision: Unified interface for Zero-Shot vision models in robotics DevFeed: [Pollen-Vision: Unified interface for Zero-Shot vision models in robotics]() Original publisher: [Read original article]() Author: Antoine Pirrone; Simon Le Goff; Rouanet; Simon Revelly Published: 2024-03-25T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Library](), [object-detection](), [Robotics](), [Open Source](), [reachy](), [real-time](), [3D](), [Google](), [Meta]() Tags: [3d](), [artificial-intelligence](), [image-tagging](), [object-detection](), [open-source](), [reachy](), [real-time](), [robotic-manipulation](), [robotics](), [robots](), [tools](), [training](), [vision](), [zero-shot]() ### AI overview Pollen-Vision is an open-source library from the Pollen Robotics team that provides a modular interface for zero-shot vision models in robotics. Its initial release combines models for 3D object detection and segmentation, producing object coordinates to support autonomous grasping and other basic manipulation tasks without additional training. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Stanford AI Lab Papers and Talks at ICLR 2022 DevFeed: [Stanford AI Lab Papers and Talks at ICLR 2022]() Original publisher: [Read original article]() Author: Compiled by Drew A. Hudson Published: 2022-04-25T07:00:00Z Content type: article Language: en Sources: [The Stanford AI Lab Blog]() Topics: [Artificial Intelligence](), [Benchmark](), [benchmarking](), [Reinforcement learning](), [dataset](), [Language models](), [Graphs](), [Robotics](), [Robotic manipulation]() Tags: [benchmark](), [benchmarking](), [datasets](), [iclr](), [knowledge-graph](), [language-models](), [neural-networks](), [reinforcement-learning](), [robotic-manipulation](), [robotics]() ### AI overview Stanford AI Lab presents its work at ICLR 2022, including papers and talks on reinforcement learning, benchmark datasets, distribution shifts, in-context learning, language models, graph reasoning, model editing, robotics, and robotic manipulation. ### Source excerpt The International Conference on Learning Representations (ICLR) 2022 is being hosted virtually from April 25th - April 29th. We're excited to share all the work from SAIL that's being presented, and you'll find links to papers, videos and blogs below. Feel free to reach out to the contact authors directly to learn more about the work that's happening at Stanford! List of Accepted Papers Autonomous Reinforcement Learning: Formalism and Benchmarking Authors: Archit Sharma*, Kelvin Xu*, Nikhil Sardana, Abhishek Gupta, Karol Hausman, Sergey Levine, Chelsea Finn Contact: architsh@stanford.edu Links: Paper | Website Keywords: reinforcement learning, continual learning, reset-free reinforcement learning MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts Authors: Weixin Liang, James Zou Contact: wxliang@stanford.edu Links: Paper | Video | Website Keywords: benchmark dataset, distribution shift, out-of-domain generalization An Explanation of In-context Learning as Implicit Bayesian Inference Authors: Sang Michael Xie, Aditi Raghunathan, Percy Liang, Tengyu Ma Contact: xie@cs.stanford.edu Links: Paper | Video Keywords: gpt-3, in-context learning, pretraining, few-shot learning GreaseLM: Graph REASoning Enhanced Language Models for Question Answering Authors: Xikun Zhang, Antoine Bosselut, Michihiro Yasunaga, Hongyu Ren, Percy Liang, Christopher D. Manning, Jure Leskovec Contact: xikunz2@cs.stanford.edu Award nominations: Spotlight Links: Paper | Website Keywords: knowledge graph, question answering, language model, commonsense reasoning, graph neural networks, biomedical qa Fast Model Editing at Scale Authors: Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, Christopher D. Manning Contact: eric.mitchell@cs.stanford.edu Links: Paper | Website Keywords: model editing; meta-learning; language models; continual learning; temporal generalization Vision-Based Manipulators Need to Also See from Their Hands Authors: Kyle H ## Stanford AI Lab Papers at CoRL 2021 DevFeed: [Stanford AI Lab Papers at CoRL 2021]() Original publisher: [Read original article]() Author: Compiled by Drew A. Hudson Published: 2021-11-05T07:00:00Z Content type: article Language: en Sources: [The Stanford AI Lab Blog]() Topics: [Reinforcement learning](), [Artificial Intelligence](), [benchmarking](), [Robotic manipulation](), [Benchmark]() Tags: [ai](), [benchmark](), [benchmarking](), [imitation](), [learning](), [offline](), [reinforcement-learning](), [rl](), [robotic-manipulation]() ### AI overview The Stanford AI Lab presents its work accepted at the Conference on Robot Learning (CoRL 2021). The article lists projects covering language-informed robot autonomy, household-activity benchmarking, human-robot collaboration, imitation learning, model-based and deep reinforcement learning, reward and preference learning, and robotic manipulation. ### Source excerpt The Conference on Robot Learning (CoRL 2021) will take place next week. We're excited to share all the work from SAIL that will be presented, and you'll find links to papers, videos and blogs below. Feel free to reach out to the contact authors directly to learn more about the work that's happening at Stanford! List of Accepted Papers LILA: Language-Informed Latent Actions Authors: Siddharth Karamcheti*, Megha Srivastava*, Percy Liang, Dorsa Sadigh Contact: skaramcheti@cs.stanford.edu, megha@cs.stanford.edu Keywords: natural language, shared autonomy, human-robot interaction BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments Authors: Sanjana Srivastava*, Chengshu Li*, Michael Lingelbach*, Roberto Martín-Martín*, Fei Xia, Kent Vainio, Zheng Lian, Cem Gokmen, Shyamal Buch, C. Karen Liu, Silvio Savarese, Hyowon Gweon, Jiajun Wu, Li Fei-Fei Contact: sanjana2@stanford.edu Links: Paper | Website Keywords: embodied ai, benchmarking, household activities Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration Authors: Chen Wang, Claudia Pérez-D'Arpino, Danfei Xu, Li Fei-Fei, C. Karen Liu, Silvio Savarese Contact: chenwj@stanford.edu Links: Paper | Website Keywords: learning for human-robot collaboration, imitation learning DiffImpact: Differentiable Rendering and Identification of Impact Sounds Authors: Samuel Clarke, Negin Heravi, Mark Rau, Ruohan Gao, Jiajun Wu, Doug James, Jeannette Bohg Contact: spclarke@stanford.edu Links: Paper | Website Keywords: differentiable sound rendering, auditory scene analysis Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks Authors: Bohan Wu, Suraj Nair, Li Fei-Fei*, Chelsea Finn* Contact: bohanwu@cs.stanford.edu Links: Paper Keywords: model-based reinforcement learning, long-horizon tasks GRAC: Self-Guided and Self-Regularized Actor-Critic Authors: Lin Shao, Yifan You, Mengyuan Yan, Shenli Yuan, Qingyun Sun, Jeannette Bohg Contact: