Datasets:
vecId stringlengths 12 23 | id stringlengths 2 13 | conference stringclasses 11
values | year float64 2.02k 2.03k | title stringlengths 6 189 | abstract stringlengths 10 4.74k | author stringlengths 0 7.45k | aff stringlengths 0 7.16k | status stringclasses 11
values | track stringclasses 4
values | keywords stringlengths 0 804 | github stringlengths 0 141 | site stringlengths 0 193 | gsCitation float64 -1 11.1k | arxiv stringlengths 0 12 | text stringlengths 58 4.82k | vector list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
corl_2023_-3G6_D66Aua | -3G6_D66Aua | corl | 2,023 | Simultaneous Learning of Contact and Continuous Dynamics | Robotic manipulation can greatly benefit from the data efficiency, robustness, and predictability of model-based methods if robots can quickly generate models of novel objects they encounter. This is especially difficult when effects like complex joint friction lack clear first-principles models and are usually ignored... | Bibit Bianchini;Mathew Halm;Michael Posa | University of Pennsylvania;School of Engineering and Applied Science, University of Pennsylvania;University of Pennsylvania | Poster | main | system identification;dynamics learning;contact-rich manipulation | https://github.com/ebianchi/dair_pll | https://openreview.net/forum?id=-3G6_D66Aua | 14 | Simultaneous Learning of Contact and Continuous Dynamics
Robotic manipulation can greatly benefit from the data efficiency, robustness, and predictability of model-based methods if robots can quickly generate models of novel objects they encounter. This is especially difficult when effects like complex joint friction l... | [
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corl_2023_-HFJuX1uqs | -HFJuX1uqs | corl | 2,023 | Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation | 3D perceptual representations are well suited for robot manipulation as they easily encode occlusions and simplify spatial reasoning. Many manipulation tasks require high spatial precision in end-effector pose prediction, which typically demands high-resolution 3D feature grids that are computationally expensive to pro... | Theophile Gervet;Zhou Xian;Nikolaos Gkanatsios;Katerina Fragkiadaki | Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University | Poster | main | Learning from Demonstrations;Manipulation;Transformers | https://github.com/zhouxian/chained-diffuser | https://openreview.net/forum?id=-HFJuX1uqs | 71 | Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation
3D perceptual representations are well suited for robot manipulation as they easily encode occlusions and simplify spatial reasoning. Many manipulation tasks require high spatial precision in end-effector pose prediction, which typically demands h... | [
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corl_2023_-K7-1WvKO3F | -K7-1WvKO3F | corl | 2,023 | ViNT: A Foundation Model for Visual Navigation | General-purpose pre-trained models (``foundation models'') have enabled practitioners to produce generalizable solutions for individual machine learning problems with datasets that are significantly smaller than those required for learning from scratch. Such models are typically trained on large and diverse datasets wi... | Dhruv Shah;Ajay Sridhar;Nitish Dashora;Kyle Stachowicz;Kevin Black;Noriaki Hirose;Sergey Levine | UC Berkeley;University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;Toyota Central R&D Labs., Inc;Google | Oral | main | visual navigation;multi-task learning;planning;generalization | https://github.com/robodhruv/visualnav-transformer | https://openreview.net/forum?id=-K7-1WvKO3F | 154 | ViNT: A Foundation Model for Visual Navigation
General-purpose pre-trained models (``foundation models'') have enabled practitioners to produce generalizable solutions for individual machine learning problems with datasets that are significantly smaller than those required for learning from scratch. Such models are typ... | [
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corl_2023_09UL1dCqf2n | 09UL1dCqf2n | corl | 2,023 | Preference learning for guiding the tree search in continuous POMDPs | A robot operating in a partially observable environment must perform sensing actions to achieve a goal, such as clearing the objects in front of a shelf to better localize a target object at the back, and estimate its shape for grasping. A POMDP is a principled framework for enabling robots to perform such information-... | Jiyong Ahn;Sanghyeon Son;Dongryung Lee;Jisu Han;Dongwon Son;Beomjoon Kim | Korea Advanced Institute of Science & Technology;;Korea Advanced Institute of Science & Technology;Korea Advanced Institute of Science & Technology;KAIST;Korea Advanced Institute of Science & Technology | Poster | main | POMDP;Online planning;Guided Search;Preference-based learning | https://openreview.net/forum?id=09UL1dCqf2n | 0 | Preference learning for guiding the tree search in continuous POMDPs
A robot operating in a partially observable environment must perform sensing actions to achieve a goal, such as clearing the objects in front of a shelf to better localize a target object at the back, and estimate its shape for grasping. A POMDP is a ... | [
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corl_2023_0I3su3mkuL | 0I3su3mkuL | corl | 2,023 | Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions | In this work, we present a scalable reinforcement learning method for training multi-task policies from large offline datasets that can leverage both human demonstrations and autonomously collected data. Our method uses a Transformer to provide a scalable representation for Q-functions trained via offline temporal diff... | Yevgen Chebotar;Quan Vuong;Karol Hausman;Fei Xia;Yao Lu;Alex Irpan;Aviral Kumar;Tianhe Yu;Alexander Herzog;Karl Pertsch;Keerthana Gopalakrishnan;Julian Ibarz;Ofir Nachum;Sumedh Anand Sontakke;Grecia Salazar;Huong T Tran;Jodilyn Peralta;Clayton Tan;Deeksha Manjunath;Jaspiar Singh;Brianna Zitkovich;Tomas Jackson;Kanishka... | Google;;;Google;Google;Google DeepMind;University of California, Berkeley;Google Brain;Google;University of Southern California;Research, Google;Google;OpenAI;University of Southern California;;;;;;;;;;Google;Google | Poster | main | Reinforcement Learning;Offline RL;Transformers;Q-Learning;Robotic Manipulation | https://openreview.net/forum?id=0I3su3mkuL | 106 | Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions
In this work, we present a scalable reinforcement learning method for training multi-task policies from large offline datasets that can leverage both human demonstrations and autonomously collected data. Our method uses a Transformer ... | [
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corl_2023_0bZaUfELuW | 0bZaUfELuW | corl | 2,023 | Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control | Our goal is for robots to follow natural language instructions like ``put the towel next to the microwave.'' But getting large amounts of labeled data, i.e. data that contains demonstrations of tasks labeled with the language instruction, is prohibitive. In contrast, obtaining policies that respond to image goals is mu... | Vivek Myers;Andre Wang He;Kuan Fang;Homer Rich Walke;Philippe Hansen-Estruch;Ching-An Cheng;Mihai Jalobeanu;Andrey Kolobov;Anca Dragan;Sergey Levine | University of California, Berkeley;UC Berkeley, University of California, Berkeley;;University of California, Berkeley;;Microsoft Research;Microsoft Research;Microsoft;University of California, Berkeley;Google | Poster | main | Instruction Following;Representation Learning;Manipulation | https://github.com/rail-berkeley/grif_release | https://openreview.net/forum?id=0bZaUfELuW | 32 | Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control
Our goal is for robots to follow natural language instructions like ``put the towel next to the microwave.'' But getting large amounts of labeled data, i.e. data that contains demonstrations of tasks labeled with the languag... | [
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corl_2023_0hPkttoGAf | 0hPkttoGAf | corl | 2,023 | RVT: Robotic View Transformer for 3D Object Manipulation | For 3D object manipulation, methods that build an explicit 3D representation perform better than those relying only on camera images. But using explicit 3D representations like voxels comes at large computing cost, adversely affecting scalability. In this work, we propose RVT, a multi-view transformer for 3D manipulati... | Ankit Goyal;Jie Xu;Yijie Guo;Valts Blukis;Yu-Wei Chao;Dieter Fox | NVIDIA;NVIDIA;University of Michigan;NVIDIA;NVIDIA;Department of Computer Science | Oral | main | 3D Manipulation;Multi-View;Transformer | https://github.com/nvlabs/rvt | https://openreview.net/forum?id=0hPkttoGAf | 140 | RVT: Robotic View Transformer for 3D Object Manipulation
For 3D object manipulation, methods that build an explicit 3D representation perform better than those relying only on camera images. But using explicit 3D representations like voxels comes at large computing cost, adversely affecting scalability. In this work, w... | [
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corl_2023_0hQMcWfjG9 | 0hQMcWfjG9 | corl | 2,023 | $\alpha$-MDF: An Attention-based Multimodal Differentiable Filter for Robot State Estimation | "Differentiable Filters are recursive Bayesian estimators that derive the state transition and measu(...TRUNCATED) | Xiao Liu;Yifan Zhou;Shuhei Ikemoto;Heni Ben Amor | "Arizona State University;Arizona State University;Kyushu Institute of Technology;Arizona State Univ(...TRUNCATED) | Poster | main | Differentiable Filters;Sensor Fusion;Multimodal Learning | https://github.com/ir-lab/alpha-MDF | https://openreview.net/forum?id=0hQMcWfjG9 | 8 | "$\\alpha$-MDF: An Attention-based Multimodal Differentiable Filter for Robot State Estimation\nDiff(...TRUNCATED) | [-0.008578325621783733,-0.037264786660671234,-0.05251111835241318,-0.02297814190387726,-0.0098684690(...TRUNCATED) | |
corl_2023_0mRSANSzEK | 0mRSANSzEK | corl | 2,023 | Improving Behavioural Cloning with Positive Unlabeled Learning | "Learning control policies offline from pre-recorded datasets is a promising avenue for solving chal(...TRUNCATED) | "Qiang Wang;Robert McCarthy;David Cordova Bulens;Kevin McGuinness;Noel E. O’Connor;Francisco Rolda(...TRUNCATED) | "University College Dublin;;;Dublin City University;;Insight Centre for Data Analytics;Max Planck In(...TRUNCATED) | Poster | main | Offline policy learning;positive unlabeled learning;behavioural cloning | https://openreview.net/forum?id=0mRSANSzEK | 8 | "Improving Behavioural Cloning with Positive Unlabeled Learning\nLearning control policies offline f(...TRUNCATED) | [-0.08705994486808777,-0.03501727804541588,-0.01797330565750599,0.014562653377652168,-0.032024826854(...TRUNCATED) | ||
corl_2023_0o2JgvlzMUc | 0o2JgvlzMUc | corl | 2,023 | Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy | "An outstanding challenge for the widespread deployment of robotic systems like autonomous vehicles (...TRUNCATED) | Haimin Hu;Zixu Zhang;Kensuke Nakamura;Andrea Bajcsy;Jaime Fernández Fisac | "Toyota Research Institute;Princeton University;Princeton University;Princeton University;University(...TRUNCATED) | Poster | main | Learning-Aware Safety Analysis;Active Information Gathering;Adversarial Reinforcement Learning | https://openreview.net/forum?id=0o2JgvlzMUc | 16 | "Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy\nAn outstanding chal(...TRUNCATED) | [-0.039126694202423096,-0.019563347101211548,-0.008261033333837986,0.019830292090773582,-0.043016482(...TRUNCATED) |
End of preview. Expand in Data Studio
Robotics Papers Vector Database
Semantic search over 63,381 academic papers from 30 conference-year combinations in robotics, CV, and ML.
Contents
- Embeddings: BAAI/bge-m3 (1024 dimensions) via SiliconFlow
- Conferences: CoRL, CVPR, ECCV, ICCV, ICLR, ICML, ICRA, IROS, NeurIPS, RSS, WACV (2023-2026)
- Fields: title, abstract, author, conference, year, arxiv, github, citations, keywords
- Format: LanceDB (compacted, single fragment)
Usage
Remote query (no download needed)
import lancedb
db = lancedb.connect("hf://datasets/Litian2002/robotics-papers-vecdb/lancedb")
table = db.open_table("papers")
print(f"Papers: {table.count_rows()}")
# Semantic search (requires embedding your query first)
# See the companion repo for the full search pipeline
Local usage
git clone https://huggingface.co/datasets/Litian2002/robotics-papers-vecdb
# or use huggingface_hub to download
With vec-db CLI
# Clone the tool repo
git clone <vec-db-repo-url>
cd vec-db
VECDB_LANCE_DIR=/path/to/downloaded/lancedb npx tsx src/cli.ts search "robot grasping"
Schema
| Column | Type | Description |
|---|---|---|
| vecId | string | {conf}_{year}_{id} compound key |
| title | string | Paper title |
| abstract | string | Paper abstract |
| author | string | Authors (semicolon-separated) |
| conference | string | Conference name |
| year | float | Publication year |
| arxiv | string | arXiv ID |
| github | string | GitHub repo URL |
| gsCitation | float | Citation count (from OpenAlex) |
| vector | float[1024] | bge-m3 embedding |
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