# CoRL
- 2024 best paper, PoliFormer: Scaling On-Policy RL with Transformers Results in Masterful Navigators, [arXiv](https://arxiv.org/abs/2406.20083)
- 2024 best paper, One Model to Drift Them All: Physics-Informed Conditional Diffusion Model for Driving at the Limits, [OpenReview](https://openreview.net/forum?id=0gDbaEtVrd)

# RSS
- 2023 best paper, Time Optimal Ergodic Search, [link](https://roboticsconference.org/2023/program/papers/082/)
- 2023 best system paper, **FurnitureBench**: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation, [link](https://roboticsconference.org/2023/program/papers/041/)
- 2023 best student paper, **Teach a Robot to FISH**: Versatile Imitation from One Minute of Demonstrations, [link](https://roboticsconference.org/2023/program/papers/009/)
- 2022 best paper, **Iterative Residual Policy**: for Goal-Conditioned Dynamic Manipulation of Deformable Objects, [arXiv](https://arxiv.org/abs/2203.00663) / [Website](https://irp.cs.columbia.edu/)
- 2022 best system paper, **Autonomously Untangling Long Cables**, [arXiv](https://arxiv.org/abs/2207.07813) / [Website](https://sites.google.com/view/rss-2022-untangling/home)
- 2022 best student paper, **AK**: Attentive Kernel for Information Gathering, [arXiv](https://arxiv.org/abs/2205.06426) / [Website](https://wchen-robotics.com/attentive_kernels/)

# ICRA
- 2024 best paper, **Goal Masked Diffusion Policies** for Unified Navigation and Exploration, [arXiv](https://arxiv.org/abs/2310.07896)
- 2024 best paper, **Open X-Embodiment**: Robotic Learning Datasets and RT-X, [arXiv](https://arxiv.org/abs/2310.08864)
- 2024 best manipulation paper, **SARA-RT**: Scaling up Robotics Transformers with Self-Adaptive Robust Attention, [arXiv](https://arxiv.org/abs/2312.01990)

# Graphics
- 2024 SIGGRAPH Best Paper, Repulsive Shells, [website](https://www.cs.cmu.edu/~kmcrane/Projects/RepulsiveShells/index.html)
- 2023 SIGGRAPH Asia best paper, Fluid Simulation on Neural Flow Maps, [Github](https://github.com/yitongdeng-projects/neural_flow_maps_code) / [Website](https://yitongdeng-projects.github.io/neural_flow_maps_webpage/)