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Author Organization: Max Planck Institute for Intelligent Systems Study Type: simulation/modelling

1 to 10 of 21 Results

MPI-10: Haptic-Auditory Measurements from Tool-Surface Interactions Jan 17, 2024 Khojasteh, Behnam; Shao, Yitian; Kuchenbecker, Katherine J., 2024, "MPI-10: Haptic-Auditory Measurements from Tool-Surface Interactions", https://doi.org/10.17617/3.PM8R94, Edmond, V1 This dataset consists of haptic-auditory recordings as a human explores 10 surfaces with 3 steel tools, including accelerations of the tool and finger, force and torque applied to the surface, and contact sounds.
Supplementary Model Files for "Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis" (EMNLP 2020) Jan 16, 2024 Jin, Zhijing, 2024, "Supplementary Model Files for "Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis" (EMNLP 2020)", https://doi.org/10.17617/3.VKWOI9, Edmond, V1 - Paper: "Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis" (EMNLP 2020) by Xiaoyu Xing, Zhijing Jin, Di Jin, Bingning Wang, Qi Zhang, and Xuanjing Huang. - Paper link: https://arxiv.org/abs/2009.07964 - Abstract: Aspect-based sentiment a...
Supplementary data for Corr2Cause: "Can Large Language Models Infer Causation from Correlation?" Jan 10, 2024 Jin, Zhijing, 2023, "Supplementary data for Corr2Cause: "Can Large Language Models Infer Causation from Correlation?"", https://doi.org/10.17617/3.VYGWHY, Edmond, V3 Paper: "Can Large Language Models Infer Causation from Correlation?" (2023) by Zhijing Jin, Jiarui Liu, Zhiheng Lyu, Spencer Poff, Mrinmaya Sachan, Rada Mihalcea, Mona Diab, Bernhard Schölkopf. (http://arxiv.org/abs/2306.05836) Abstract: Causal inference is one of the hallmarks...
CLadder: Assessing Causal Reasoning in Language Models Jan 7, 2024 Jin, Zhijing, 2024, "CLadder: Assessing Causal Reasoning in Language Models", https://doi.org/10.17617/3.NVRRA9, Edmond, V1 Paper: "CLadder: Assessing Causal Reasoning in Language Models" (NeurIPS 2023) by Zhijing Jin, Yuen Chen, Felix Leeb, Luigi Gresele, Ojasv Kamal, Zhiheng Lyu, Kevin Blin, Fernando Gonzalez, Max Kleiman-Weiner, Mrinmaya Sachan, Bernhard Schölkopf. (http://arxiv.org/abs/2312.04...
GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation Jan 2, 2024 Jin, Zhijing, 2024, "GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation", https://doi.org/10.17617/3.YGO7EW, Edmond, V1 Paper: "GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation" (COLING 2020) by Zhijing Jin, Qipeng Guo, Xipeng Qiu, and Zheng Zhang. (https://aclanthology.org/2020.coling-main.217/) Abstract: Data collection for the knowledge...
Data for Upside down: an open-source motion platform for highly dynamic movement Jul 26, 2023 Man-Singh-Pradhan, Nayan; Frank, Patrick; Mo, An; Badri-Spröwitz, Alexander, 2023, "Data for Upside down: an open-source motion platform for highly dynamic movement", https://doi.org/10.17617/3.P9GM9Z, Edmond, V1 Dataset contains simulation model, control program, and CAD model of the motion platform. Publication link: https://doi.org/10.48550/arXiv.2303.17974
Training checkpoints for the TriFinger Cube Push and Lift tasks Jul 14, 2023 Gürtler, Nico, 2023, "Training checkpoints for the TriFinger Cube Push and Lift tasks", https://doi.org/10.17617/3.JA8ZW4, Edmond, V1 Training checkpoints for policies trained in Isaac Gym simulation of the TriFinger robotics platform. Checkpoints are available for two tasks: (i) Push a cube to a goal position on the ground, (ii) Lift a cube to goal position and orientation in the air. The checkpoints can be us...
TriFinger RL Datasets May 19, 2023 Gürtler, Nico; Blaes, Sebastian; Kolev, Pavel; Widmaier, Felix; Wuthrich, Manuel; Bauer, Stefan; Schölkopf, Bernhard; Martius, Georg, 2023, "TriFinger RL Datasets", https://doi.org/10.17617/3.DXZ7TL, Edmond, V1 Offline reinforcement learning datasets collected on real TriFinger robots and with a simulated version of the robot. Trajectories for two dexterous manipulation tasks were collected: Pushing a cube to a goal position on the ground and Lifting it to a goal position and orientatio...
Learn2Feel Apr 21, 2023 Richardson, Benjamin; Vardar, Yasemin; Wallraven, Christian; Kuchenbecker, Katherine, 2022, "Learn2Feel", https://doi.org/10.17617/3.2HBHR8, Edmond, V2 Raw data, windowed data, and features extracted from windowed data: from the force/torque and accelerometers during human exploration of surfaces.
Penn Haptic Adjective Corpus 2 Mar 27, 2023 Richardson, Ben; McMahon, Ian; Chu, Vivian; Martinez Perez-Tejado, Jorge; Arrigo, Michael; Kuchenbecker, Katherine J., 2023, "Penn Haptic Adjective Corpus 2", https://doi.org/10.17617/3.0C79KW, Edmond, V1 Haptic exploratory data of 60 diverse objects, systematically acquired object images, and data parsing code. The file 'phac_train_test_pos_neg_90_10_1_20.h5' contains the raw robot sensor data and binary adjective labels (25 total adjectives). Use processPHAC2.m to parse this h5...

MPI-10: Haptic-Auditory Measurements from Tool-Surface Interactions

Jan 17, 2024

Khojasteh, Behnam; Shao, Yitian; Kuchenbecker, Katherine J., 2024, "MPI-10: Haptic-Auditory Measurements from Tool-Surface Interactions", https://doi.org/10.17617/3.PM8R94, Edmond, V1

This dataset consists of haptic-auditory recordings as a human explores 10 surfaces with 3 steel tools, including accelerations of the tool and finger, force and torque applied to the surface, and contact sounds.

Supplementary Model Files for "Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis" (EMNLP 2020)

Jan 16, 2024

Jin, Zhijing, 2024, "Supplementary Model Files for "Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis" (EMNLP 2020)", https://doi.org/10.17617/3.VKWOI9, Edmond, V1

- Paper: "Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis" (EMNLP 2020) by Xiaoyu Xing*, Zhijing Jin*, Di Jin, Bingning Wang, Qi Zhang, and Xuanjing Huang. - Paper link: https://arxiv.org/abs/2009.07964 - Abstract: Aspect-based sentiment a...

Supplementary data for Corr2Cause: "Can Large Language Models Infer Causation from Correlation?"

Jan 10, 2024

Jin, Zhijing, 2023, "Supplementary data for Corr2Cause: "Can Large Language Models Infer Causation from Correlation?"", https://doi.org/10.17617/3.VYGWHY, Edmond, V3

Paper: "Can Large Language Models Infer Causation from Correlation?" (2023) by Zhijing Jin, Jiarui Liu, Zhiheng Lyu, Spencer Poff, Mrinmaya Sachan, Rada Mihalcea, Mona Diab*, Bernhard Schölkopf*. (http://arxiv.org/abs/2306.05836) Abstract: Causal inference is one of the hallmarks...

CLadder: Assessing Causal Reasoning in Language Models

Jan 7, 2024

Jin, Zhijing, 2024, "CLadder: Assessing Causal Reasoning in Language Models", https://doi.org/10.17617/3.NVRRA9, Edmond, V1

Paper: "CLadder: Assessing Causal Reasoning in Language Models" (NeurIPS 2023) by Zhijing Jin*, Yuen Chen*, Felix Leeb*, Luigi Gresele*, Ojasv Kamal, Zhiheng Lyu, Kevin Blin, Fernando Gonzalez, Max Kleiman-Weiner, Mrinmaya Sachan, Bernhard Schölkopf. (http://arxiv.org/abs/2312.04...

GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation

Jan 2, 2024

Jin, Zhijing, 2024, "GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation", https://doi.org/10.17617/3.YGO7EW, Edmond, V1

Paper: "GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation" (COLING 2020) by Zhijing Jin, Qipeng Guo, Xipeng Qiu, and Zheng Zhang. (https://aclanthology.org/2020.coling-main.217/) Abstract: Data collection for the knowledge...

Data for Upside down: an open-source motion platform for highly dynamic movement

Jul 26, 2023

Man-Singh-Pradhan, Nayan; Frank, Patrick; Mo, An; Badri-Spröwitz, Alexander, 2023, "Data for Upside down: an open-source motion platform for highly dynamic movement", https://doi.org/10.17617/3.P9GM9Z, Edmond, V1

Dataset contains simulation model, control program, and CAD model of the motion platform. Publication link: https://doi.org/10.48550/arXiv.2303.17974

Training checkpoints for the TriFinger Cube Push and Lift tasks

Jul 14, 2023

Gürtler, Nico, 2023, "Training checkpoints for the TriFinger Cube Push and Lift tasks", https://doi.org/10.17617/3.JA8ZW4, Edmond, V1

Training checkpoints for policies trained in Isaac Gym simulation of the TriFinger robotics platform. Checkpoints are available for two tasks: (i) Push a cube to a goal position on the ground, (ii) Lift a cube to goal position and orientation in the air. The checkpoints can be us...

TriFinger RL Datasets

May 19, 2023

Gürtler, Nico; Blaes, Sebastian; Kolev, Pavel; Widmaier, Felix; Wuthrich, Manuel; Bauer, Stefan; Schölkopf, Bernhard; Martius, Georg, 2023, "TriFinger RL Datasets", https://doi.org/10.17617/3.DXZ7TL, Edmond, V1

Offline reinforcement learning datasets collected on real TriFinger robots and with a simulated version of the robot. Trajectories for two dexterous manipulation tasks were collected: Pushing a cube to a goal position on the ground and Lifting it to a goal position and orientatio...

Learn2Feel

Apr 21, 2023

Richardson, Benjamin; Vardar, Yasemin; Wallraven, Christian; Kuchenbecker, Katherine, 2022, "Learn2Feel", https://doi.org/10.17617/3.2HBHR8, Edmond, V2

Raw data, windowed data, and features extracted from windowed data: from the force/torque and accelerometers during human exploration of surfaces.

Penn Haptic Adjective Corpus 2

Mar 27, 2023

Richardson, Ben; McMahon, Ian; Chu, Vivian; Martinez Perez-Tejado, Jorge; Arrigo, Michael; Kuchenbecker, Katherine J., 2023, "Penn Haptic Adjective Corpus 2", https://doi.org/10.17617/3.0C79KW, Edmond, V1

Haptic exploratory data of 60 diverse objects, systematically acquired object images, and data parsing code. The file 'phac_train_test_pos_neg_90_10_1_20.h5' contains the raw robot sensor data and binary adjective labels (25 total adjectives). Use processPHAC2.m to parse this h5...

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