Author Organization: Max Planck Institute for Intelligent Systems
Publication Year: 2023
Study Type: simulation/modelling
1 to 7 of 7 Results
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... |
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 |
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... |
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... |
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... |
Jan 20, 2023
Spiers, Ad, 2023, "S-BAN Haptic Interface Open Source CAD", https://doi.org/10.17617/3.LERWAX, Edmond, V1
The S-BAN (Shaped-Based Assistance for Navigation) is a shape-changing handheld navigation device created in the Haptic Intelligence Department. The device is introduced in the following paper: https://dl.acm.org/doi/abs/10.1145/3555046 The attached CAD folder includes: .STL file... |
Jan 11, 2023
Ruggeri, Nicolo, 2023, "Real and Synthetic data for Hypergraph Benchmarking (processed for the Hy-MMSBM generative model)", https://doi.org/10.17617/3.HRW0OE, Edmond, V2
This dataset contains the preprocessed real hypergraph data utilized in the experiments for "Generalized inference of mesoscale structures in higher-order networks", Ruggeri N., Contisciani M., Battiston F., De Bacco C. and synthetic hypergraph data generated for experiments in t... |